From 7fd9f50ca5e2c56d33568b55a2f56514c0095db9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6khan?= <81560784+gokhanturer@users.noreply.github.com> Date: Thu, 15 Jun 2023 10:54:52 +0300 Subject: [PATCH] Models hub internal (#390) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 2023-03-29-icd10cm_resolver_pipeline_en (#44) * Add model 2023-03-29-icd10cm_resolver_pipeline_en * Add model 2023-03-29-icd10cm_resolver_pipeline_en --------- Co-authored-by: Ahmetemintek * 2023-03-29-oncology_general_pipeline_en (#46) * Add model 2023-03-29-oncology_general_pipeline_en * Add model 2023-03-29-oncology_diagnosis_pipeline_en * Add model 2023-03-29-oncology_biomarker_pipeline_en * Add model 2023-03-29-icd10_icd9_mapping_en * Add model 2023-03-29-icd10cm_snomed_mapping_en * Add model 2023-03-29-icd10cm_umls_mapping_en * Add model 2023-03-29-icd9_resolver_pipeline_en * Add model 2023-03-29-icdo_snomed_mapping_en * Add model 2023-03-29-mesh_umls_mapping_en * Add model 2023-03-29-rxnorm_ndc_mapping_en * Add model 2023-03-29-rxnorm_umls_mapping_en * Add model 2023-03-29-snomed_icd10cm_mapping_en * Add model 2023-03-29-snomed_icd10cm_mapping_en * Add model 2023-03-29-snomed_icdo_mapping_en * Add model 2023-03-29-oncology_therapy_pipeline_en * Add model 2023-03-29-snomed_umls_mapping_en --------- Co-authored-by: Cabir40 * 2023-03-29-icd10cm_resolver_pipeline_en (#45) * Add model 2023-03-29-icd10cm_resolver_pipeline_en * Add model 2023-03-29-umls_drug_resolver_pipeline_en --------- Co-authored-by: Ahmetemintek * Add model 2023-03-30-text_generator_biomedical_biogpt_en (#48) Co-authored-by: Cabir40 * 2023-03-30-summarizer_generic_jsl_en (#49) * Add model 2023-03-30-summarizer_generic_jsl_en * Update 2023-03-30-summarizer_generic_jsl_en.md --------- Co-authored-by: Cabir40 Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com> * Add model 2023-03-30-summarizer_clinical_jsl_augmented_en (#51) * Update 2023-03-30-summarizer_generic_jsl_en.md * Update 2023-03-30-text_generator_biomedical_biogpt_en.md * Update 2023-03-30-summarizer_clinical_jsl_augmented_en.md * 2023-04-02-ner_jsl_limited_80p_for_benchmarks_en (#58) * Add model 2023-04-02-ner_jsl_limited_80p_for_benchmarks_en * Update 2023-04-02-ner_jsl_limited_80p_for_benchmarks_en.md * Update 2023-04-02-ner_jsl_limited_80p_for_benchmarks_en.md --------- Co-authored-by: Damla-Gurbaz Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com> Co-authored-by: Veysel Kocaman * 2023-03-30-ner_vop_wip_en (#52) * Add model 2023-03-30-ner_vop_wip_en * Update 2023-03-30-ner_vop_wip_en.md --------- Co-authored-by: mauro-nievoff Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com> * 2023-03-30-umls_clinical_findings_resolver_pipeline_en (#47) * Add model 2023-03-30-umls_clinical_findings_resolver_pipeline_en * Add model 2023-03-30-cvx_resolver_pipeline_en * Add model 2023-03-30-umls_disease_syndrome_resolver_pipeline_en * Add model 2023-03-30-umls_major_concepts_resolver_pipeline_en * Add model 2023-03-30-umls_drug_substance_resolver_pipeline_en * Add model 2023-03-31-medication_resolver_pipeline_en * Add model 2023-03-31-medication_resolver_transform_pipeline_en * Add model 2023-03-31-spellcheck_clinical_pipeline_en --------- Co-authored-by: Ahmetemintek * Add model 2023-04-03-ner_oncology_limited_80p_for_benchmarks_en (#61) * 2023-04-03-text_generator_biomedical_biogpt_base_en (#60) * Delete 2023-03-30-text_generator_biomedical_biogpt_en.md * Update 2023-04-03-text_generator_generic_flan_base_en.md * Update 2023-03-30-summarizer_clinical_jsl_augmented_en.md * Update 2023-03-30-summarizer_generic_jsl_en.md * Add model 2023-04-04-text_generator_generic_flan_t5_large_en (#64) * 2023-04-07-embeddings_clinical_medium_en (#75) * Add model 2023-04-07-embeddings_clinical_medium_en * Update 2023-04-07-embeddings_clinical_medium_en.md --------- Co-authored-by: Cabir40 Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com> * Add model 2023-04-07-embeddings_clinical_large_en (#79) * 2023-04-10-genericclassifier_sdoh_mental_health_embeddings_clinical_en (#81) * Add model 2023-04-10-genericclassifier_sdoh_mental_health_embeddings_clinical_en * Add model 2023-04-10-genericclassifier_sdoh_under_treatment_sbiobert_cased_mli_en * Add model 2023-04-10-genericclassifier_sdoh_housing_insecurity_sbiobert_cased_mli_en * added missing md files * Add model 2023-04-10-genericclassifier_sdoh_mental_health_clinical_en * Delete 2023-04-10-genericclassifier_sdoh_mental_health_embeddings_clinical_en.md * Update 2023-04-10-genericclassifier_sdoh_under_treatment_sbiobert_cased_mli_en.md * Update 2023-04-10-genericclassifier_sdoh_mental_health_clinical_en.md * Update 2023-04-10-genericclassifier_sdoh_housing_insecurity_sbiobert_cased_mli_en.md * Update 2023-04-10-genericclassifier_sdoh_under_treatment_sbiobert_cased_mli_en.md * Update 2023-04-10-genericclassifier_sdoh_mental_health_clinical_en.md --------- Co-authored-by: hsaglamlar Co-authored-by: Cabir ÇELİK Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com> * 2023-04-10-medication_resolver_pipeline_en (#83) * Add model 2023-04-10-medication_resolver_pipeline_en * Add model 2023-04-11-medication_resolver_transform_pipeline_en --------- Co-authored-by: SKocer * 2023-04-11-umls_clinical_findings_resolver_pipeline_en (#89) * Add model 2023-04-11-umls_clinical_findings_resolver_pipeline_en * Add model 2023-04-11-umls_drug_substance_resolver_pipeline_en --------- Co-authored-by: Cabir40 * 2023-04-12-ner_vop_emb_clinical_large_wip_en (#100) * Add model 2023-04-12-ner_vop_emb_clinical_large_wip_en * Add model 2023-04-12-ner_vop_emb_clinical_medium_wip_en --------- Co-authored-by: mauro-nievoff * Add model 2023-04-12-biogpt_chat_jsl_en (#103) Co-authored-by: Cabir40 * 2023-04-12-ner_oncology_emb_clinical_large_en (#101) * Add model 2023-04-12-ner_oncology_emb_clinical_large_en * Add model 2023-04-12-ner_oncology_emb_clinical_medium_en --------- Co-authored-by: Meryem1425 * 2023-04-12-ner_posology_emb_clinical_large_en (#99) * Add model 2023-04-12-ner_posology_emb_clinical_large_en * Update 2023-04-12-ner_posology_emb_clinical_large_en.md Fixed the given sample text * Add model 2023-04-12-ner_posology_emb_clinical_medium_en * Update 2023-04-12-ner_posology_emb_clinical_medium_en.md Corrected thr code block view * Add model 2023-04-12-ner_deid_large_emb_clinical_large_en * Update 2023-04-12-ner_deid_large_emb_clinical_large_en.md Corrected the code block view * Add model 2023-04-12-ner_deid_large_emb_clinical_medium_en * Update 2023-04-12-ner_deid_large_emb_clinical_medium_en.md corrected the python code block view * Update 2023-04-12-ner_posology_emb_clinical_medium_en.md Fixed scala code * Update 2023-04-12-ner_posology_emb_clinical_large_en.md Fixed scala code --------- Co-authored-by: gokhanturer Co-authored-by: Gökhan <81560784+gokhanturer@users.noreply.github.com> * 2023-04-12-ner_jsl_emb_clinical_medium_en (#98) * Add model 2023-04-12-ner_jsl_emb_clinical_medium_en * Add model 2023-04-12-ner_jsl_emb_clinical_large_en --------- Co-authored-by: Damla-Gurbaz * 2023-04-12-ner_sdoh_emb_clinical_medium_wip_en (#96) * Add model 2023-04-12-ner_sdoh_emb_clinical_medium_wip_en * Add model 2023-04-12-ner_sdoh_emb_clinical_large_wip_en --------- Co-authored-by: hsaglamlar * 2023-04-13-ndc_hcpcs_mapper_en (#113) * Add model 2023-04-13-ndc_hcpcs_mapper_en * Add model 2023-04-13-hcpcs_ndc_mapper_en --------- Co-authored-by: Ahmetemintek * Add model 2023-04-13-medication_resolver_pipeline_en (#110) Co-authored-by: Cabir40 * Add model 2023-04-17-ner_sdoh_emb_clinical_large_wip_en (#126) * Add model 2023-04-18-biogpt_chat_jsl_conversational_en (#130) Co-authored-by: HashamUlHaq * 2023-04-20-explain_clinical_doc_medication_en (#140) * Add model 2023-04-20-explain_clinical_doc_medication_en * Add model 2023-04-20-explain_clinical_doc_radiology_en * Add model 2023-04-20-explain_clinical_doc_ade_en * Add model 2023-04-20-explain_clinical_doc_era_en * Add model 2023-04-20-explain_clinical_doc_carp_en --------- Co-authored-by: Cabir40 * 2023-04-23-summarizer_jsl_radiology_en (#145) * Update 2023-04-23-summarizer_jsl_radiology_en.md * 2023-04-20-ner_vop_anatomy_wip_en (#139) * 2023-04-26-ner_profiling_biobert_en (#151) * 2023-04-27-genericclassifier_sdoh_housing_insecurity_sbiobert_cased_mli_en (#168) * Update 2023-04-23-summarizer_jsl_radiology_en.md * 2023-04-27-genericclassifier_sdoh_insurance_status_sbiobert_cased_mli_en (#170) * Add model 2023-04-27-genericclassifier_sdoh_insurance_status_sbiobert_cased_mli_en * Add model 2023-04-28-genericclassifier_sdoh_insurance_type_sbiobert_cased_mli_en * Add model 2023-04-28-genericclassifier_sdoh_insurance_coverage_sbiobert_cased_mli_en * Update 2023-04-27-genericclassifier_sdoh_insurance_status_sbiobert_cased_mli_en.md * Update 2023-04-28-genericclassifier_sdoh_insurance_type_sbiobert_cased_mli_en.md --------- Co-authored-by: hsaglamlar Co-authored-by: Halil Saglamlar <47859156+hsaglamlar@users.noreply.github.com> * Add model 2023-05-01-bert_token_classifier_ner_jsl_en (#181) Co-authored-by: Damla-Gurbaz * Add model 2023-04-28-icd10cm_resolver_pipeline_en (#174) Co-authored-by: Meryem1425 * 2023-04-28-ner_profiling_biobert_en (#171) * Add model 2023-04-28-ner_profiling_biobert_en * Add model 2023-04-28-ner_profiling_clinical_en --------- Co-authored-by: Cabir40 * Add model 2023-05-02-ner_demographic_extended_en (#187) Co-authored-by: andrei9825 * Add model 2023-05-04-classifier_logreg_ade_en (#194) Co-authored-by: gpirge * 2023-05-04-ner_profiling_clinical_en (#196) * Update 2023-05-04-bert_token_classifier_ner_jsl_en.md Add benchmark * fixed typos on 2023-05-04-bert_token_classifier_ner_jsl_en.md * 2023-05-05-ner_clinical_de (#199) * 2023-05-04-classifierml_ade_en (#197) * 2023-05-08-summarizer_clinical_key_facts_en (#201) * Add model 2023-05-08-summarizer_clinical_key_facts_en * Update 2023-05-08-summarizer_clinical_key_facts_en.md changed model name * Update 2023-05-08-summarizer_clinical_key_facts_en.md Changed description --------- Co-authored-by: HashamUlHaq Co-authored-by: Damla Gurbaz <81505007+Damla-Gurbaz@users.noreply.github.com> * renamed md file * 2023-05-09-generic_logreg_classifier_ade_en (#202) * Add model 2023-05-09-generic_logreg_classifier_ade_en * Update 2023-05-09-generic_logreg_classifier_ade_en.md corrected the indentations of the Python codes * Update 2023-05-09-generic_logreg_classifier_ade_en.md changed the title * Add model 2023-05-09-generic_svm_classifier_ade_en --------- Co-authored-by: gpirge Co-authored-by: gursev.pirge <67619330+gpirge@users.noreply.github.com> * Add model 2023-05-08-ner_clinical_de (#200) Co-authored-by: Ahmetemintek * Add model 2023-05-11-biogpt_chat_jsl_conditions_en (#204) Co-authored-by: HashamUlHaq * 2023-05-11-classifier_logreg_ade_en (#209) * Add model 2023-05-11-classifier_logreg_ade_en * Add model 2023-05-11-classifierml_ade_en --------- Co-authored-by: gpirge * 2023-05-12-ner_abbreviation_emb_clinical_medium_en (#214) * Add model 2023-05-12-ner_abbreviation_emb_clinical_medium_en * Add model 2023-05-12-ner_abbreviation_emb_clinical_large_en * Update 2023-05-12-ner_abbreviation_emb_clinical_medium_en.md * Update 2023-05-12-ner_abbreviation_emb_clinical_large_en.md --------- Co-authored-by: Damla-Gurbaz Co-authored-by: Damla Gurbaz <81505007+Damla-Gurbaz@users.noreply.github.com> * 2023-05-11-icd10cm_cause_claim_mapper_en (#210) * Add model 2023-05-11-icd10cm_cause_claim_mapper_en * Update 2023-05-11-icd10cm_cause_claim_mapper_en.md The provided example is updated based on the review. Additional explanation is added for the case where there is no equivalent claim analysis code. * Update 2023-05-11-icd10cm_cause_claim_mapper_en.md * Update 2023-05-11-icd10cm_cause_claim_mapper_en.md * Update 2023-05-11-icd10cm_cause_claim_mapper_en.md * Update 2023-05-11-icd10cm_cause_claim_mapper_en.md The output results are changed. --------- Co-authored-by: SKocer Co-authored-by: Samed K <110497137+SKocer@users.noreply.github.com> Co-authored-by: muhammetsnts <76607915+muhammetsnts@users.noreply.github.com> * Add model 2023-05-13-spellcheck_drug_norvig_en (#220) Co-authored-by: Ahmetemintek * Update 2023-05-08-summarizer_clinical_guidelines_large_en.md * 2023-05-15-ner_anatomy_emb_clinical_medium_en (#225) * Add model 2023-05-15-ner_anatomy_emb_clinical_medium_en * Add model 2023-05-15-ner_anatomy_emb_clinical_large_en * Update 2023-05-15-ner_anatomy_emb_clinical_medium_en.md * Update 2023-05-15-ner_anatomy_emb_clinical_large_en.md * Update 2023-05-15-ner_anatomy_emb_clinical_medium_en.md * Update 2023-05-15-ner_anatomy_emb_clinical_large_en.md * Update 2023-05-15-ner_anatomy_emb_clinical_medium_en.md --------- Co-authored-by: gokhanturer Co-authored-by: Gökhan <81560784+gokhanturer@users.noreply.github.com> * 2023-05-15-biogpt_pubmed_qa_en (#227) * Add model 2023-05-15-biogpt_pubmed_qa_en * Update 2023-05-15-biogpt_pubmed_qa_en.md * Add model 2023-05-15-flan_t5_base_jsl_qa_en * Update 2023-05-15-flan_t5_base_jsl_qa_en.md --------- Co-authored-by: Cabir40 Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com> * Add model 2023-05-17-medical_qa_biogpt_en (#234) Co-authored-by: Cabir40 * Update 2023-04-20-explain_clinical_doc_carp_en.md * Update 2023-04-20-explain_clinical_doc_era_en.md * Update 2023-04-20-explain_clinical_doc_carp_en.md * Update 2023-04-28-icd10cm_resolver_pipeline_en.md * Update 2023-05-08-summarizer_clinical_guidelines_large_en.md * 2023-05-20-sbiobertresolve_icd10cm_augmented_en (#243) * Add model 2023-05-20-sbiobertresolve_icd10cm_augmented_en * Update 2023-05-20-sbiobertresolve_icd10cm_augmented_en.md * Update 2023-05-20-sbiobertresolve_icd10cm_augmented_en.md * Update 2023-05-20-sbiobertresolve_icd10cm_augmented_en.md --------- Co-authored-by: Ahmetemintek Co-authored-by: muhammetsnts <76607915+muhammetsnts@users.noreply.github.com> * 2023-05-21-ner_ade_emb_clinical_medium_en (#244) * Add model 2023-05-21-ner_ade_emb_clinical_medium_en * Add model 2023-05-21-ner_ade_emb_clinical_large_en * Add model 2023-05-24-ner_cellular_emb_clinical_medium_en * Add model 2023-05-24-ner_cellular_emb_clinical_large_en --------- Co-authored-by: Damla-Gurbaz * 2023-05-23-ner_bacterial_species_emb_clinical_medium_en (#252) * Add model 2023-05-23-ner_bacterial_species_emb_clinical_medium_en * Update 2023-05-23-ner_bacterial_species_emb_clinical_medium_en.md * Add model 2023-05-23-ner_bacterial_species_emb_clinical_large_en * Update 2023-05-23-ner_bacterial_species_emb_clinical_large_en.md * Update 2023-05-23-ner_bacterial_species_emb_clinical_medium_en.md * Update 2023-05-23-ner_bacterial_species_emb_clinical_large_en.md * Update 2023-05-23-ner_bacterial_species_emb_clinical_medium_en.md --------- Co-authored-by: gokhanturer Co-authored-by: Gökhan <81560784+gokhanturer@users.noreply.github.com> * 2023-05-19-ner_vop_wip_en (#242) * Add model 2023-05-19-ner_vop_wip_en * Add model 2023-05-19-ner_vop_wip_embeddings_clinical_medium_en * Add model 2023-05-19-ner_vop_wip_embeddings_clinical_large_en * Add model 2023-05-19-ner_vop_anatomy_wip_en * Add model 2023-05-19-ner_vop_clinical_dept_wip_en * Add model 2023-05-19-ner_vop_demographic_wip_en * Add model 2023-05-19-ner_vop_problem_reduced_wip_en * Add model 2023-05-19-ner_vop_problem_wip_en * Add model 2023-05-19-ner_vop_temporal_wip_en * Add model 2023-05-19-ner_vop_test_wip_en * Add model 2023-05-19-ner_vop_treatment_wip_en * Update 2023-05-19-ner_vop_anatomy_wip_en.md Fixing wrong special character ("). * Update 2023-05-19-ner_vop_treatment_wip_en.md Fixing special character (" to '). * Update 2023-05-19-ner_vop_test_wip_en.md Fixing special character (" to '). * Update 2023-05-19-ner_vop_temporal_wip_en.md Special character fix (" to '). * Update 2023-05-19-ner_vop_problem_wip_en.md Special character fix (" to '). * Update 2023-05-19-ner_vop_problem_reduced_wip_en.md Special character fix (" to '). * Update 2023-05-19-ner_vop_demographic_wip_en.md Special character fix (" to '). * Update 2023-05-19-ner_vop_clinical_dept_wip_en.md Adding sample text. * Update 2023-05-19-ner_vop_wip_embeddings_clinical_large_en.md Special character fix (" to '). * Update 2023-05-19-ner_vop_wip_embeddings_clinical_medium_en.md Special character fix (" to '). * Update 2023-05-19-ner_vop_wip_en.md Special character fix (" to '). * Update 2023-05-19-ner_vop_wip_embeddings_clinical_medium_en.md Changing model name ner_vop_wip_embeddings_clinical_medium to ner_vop_wip_emb_clinical_medium. * Update 2023-05-19-ner_vop_wip_embeddings_clinical_large_en.md Changing model name ner_vop_wip_embeddings_clinical_large to ner_vop_wip_emb_clinical_large. * Update 2023-05-19-ner_vop_wip_embeddings_clinical_medium_en.md Changed s3 link. * Update 2023-05-19-ner_vop_wip_embeddings_clinical_large_en.md Changed s3 link. * Add model 2023-05-24-bert_sequence_classifier_vop_side_effect_en * Update 2023-05-24-bert_sequence_classifier_vop_side_effect_en.md * Update 2023-05-24-bert_sequence_classifier_vop_side_effect_en.md Minor edit. * Update 2023-05-24-bert_sequence_classifier_vop_side_effect_en.md Minor edit. --------- Co-authored-by: mauro-nievoff Co-authored-by: mauro-nievoff <55700369+mauro-nievoff@users.noreply.github.com> * 2023-05-24-sbiobertresolve_icd10cm_generalised_augmented_en (#256) * 2023-05-29-summarizer_clinical_jsl_pipeline_en (#282) * 2023-05-29-arabic_medical_ner_model_specific_17_entities_ar (#283) * Add model 2023-05-29-arabic_medical_ner_model_specific_17_entities_ar * Update 2023-05-29-arabic_medical_ner_model_specific_17_entities_ar.md * update md file name * Add model 2023-05-30-ner_deid_generic_ar * fixed typos * fixed typos --------- Co-authored-by: mellahysf Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com> Co-authored-by: Cabir ÇELİK * 2023-05-29-summarizer_clinical_layman_en (#280) * Add model 2023-05-29-summarizer_clinical_layman_en * Update 2023-05-29-summarizer_clinical_layman_en.md * update model name * Added English names to the example text --------- Co-authored-by: dcecchini Co-authored-by: Cabir ÇELİK * 2023-05-26-icd10cm_billable_hcc_mapper_en (#266) * Add model 2023-05-26-icd10cm_billable_hcc_mapper_en * Update 2023-05-26-icd10cm_billable_hcc_mapper_en.md md file was fixed based on the feedback. * Update 2023-05-26-icd10cm_billable_hcc_mapper_en.md --------- Co-authored-by: Ahmetemintek Co-authored-by: muhammetsnts <76607915+muhammetsnts@users.noreply.github.com> * 2023-05-16-classifier_logreg_ade_en (#232) * 2023-05-30-genericclassifier_sdoh_housing_insecurity_sbiobert_cased_mli_en (#299) * 2023-05-30-icd10cm_mapper_en (#295) * 2023-05-31-sbiobertresolve_hcc_augmented_en (#304) * Add model 2023-05-31-sbiobertresolve_hcc_augmented_en * Update 2023-05-31-sbiobertresolve_hcc_augmented_en.md --------- Co-authored-by: Ahmetemintek * 2023-05-31-summarizer_clinical_guidelines_large_pipeline_en (#305) * Add model 2023-05-31-summarizer_clinical_guidelines_large_pipeline_en * Add model 2023-05-31-summarizer_radiology_pipeline_en * Add model 2023-05-31-summarizer_clinical_questions_pipeline_en * Add model 2023-05-31-summarizer_biomedical_pubmed_pipeline_en * Delete 2023-05-31-summarizer_biomedical_pubmed_pipeline_en.md * Add model 2023-05-31-summarizer_generic_jsl_pipeline_en * Add model 2023-05-31-summarizer_biomedical_pubmed_pipeline_en * Add model 2023-05-31-summarizer_clinical_jsl_augmented_pipeline_en * Add model 2023-05-31-summarizer_clinical_jsl_pipeline_en --------- Co-authored-by: Damla-Gurbaz Co-authored-by: Damla Gurbaz <81505007+Damla-Gurbaz@users.noreply.github.com> * 2023-05-31-sbertresolve_icd10cm_slim_billable_hcc_en (#302) * 2023-05-31-sbertresolve_icd10cm_augmented_en (#306) * Update 2023-05-26-icd10cm_billable_hcc_mapper_en.md * 2023-05-31-ner_deid_subentity_pipeline_ar (#313) * Add model 2023-05-31-ner_deid_subentity_pipeline_ar * Add model 2023-05-31-ner_deid_generic_pipeline_ar --------- Co-authored-by: mellahysf * 2023-05-31-ner_deid_subentity_pipeline_ar (#313) * Add model 2023-05-31-ner_deid_subentity_pipeline_ar * Add model 2023-05-31-ner_deid_generic_pipeline_ar * Update 2023-05-31-ner_deid_subentity_pipeline_ar.md --------- Co-authored-by: mellahysf Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com> * 2023-06-02-ner_chemicals_emb_clinical_large_en (#327) * Add model 2023-06-06-ner_deid_generic_pipeline_ar (#351) * 2023-06-06-ner_vop_en (#350) * Add model 2023-06-08-ner_demographic_extended_healthcare_en (#359) Co-authored-by: Cabir40 * 2023-06-06-summarizer_clinical_laymen_pipeline_en (#352) * Add model 2023-06-06-summarizer_clinical_laymen_pipeline_en * Add model 2023-06-06-bert_token_classifier_ner_jsl_pipeline_en * Add model 2023-06-06-bert_token_classifier_ner_jsl_pipeline_en * Add model 2023-06-07-bert_token_classifier_ner_jsl_pipeline_en --------- Co-authored-by: Damla-Gurbaz * Add model 2023-06-11-ner_profiling_biobert_en (#362) Co-authored-by: ahmedlone127 * 2023-06-09-ner_vop_pipeline_en (#361) * Add model 2023-06-09-ner_vop_pipeline_en * Add model 2023-06-09-ner_vop_anatomy_pipeline_en * Add model 2023-06-10-ner_vop_clinical_dept_pipeline_en * Add model 2023-06-10-ner_vop_demographic_pipeline_en * Add model 2023-06-10-ner_vop_problem_pipeline_en * Add model 2023-06-10-ner_vop_problem_reduced_pipeline_en * Add model 2023-06-10-ner_vop_temporal_pipeline_en * Add model 2023-06-10-ner_vop_test_pipeline_en * Add model 2023-06-10-ner_vop_treatment_pipeline_en --------- Co-authored-by: mauro-nievoff * Add model 2023-06-13-ner_sdoh_en (#370) Co-authored-by: hsaglamlar * Add model 2023-06-13-icd10cm_umls_mapping_en (#373) Co-authored-by: C-K-Loan * 2023-06-13-ner_profiling_biobert_en (#368) * Add model 2023-06-13-ner_profiling_biobert_en * Add model 2023-06-13-explain_clinical_doc_carp_en * Add model 2023-06-13-explain_clinical_doc_era_en * Add model 2023-06-13-snomed_icd10cm_mapping_en * Add model 2023-06-13-icd10cm_umls_mapping_en * Add model 2023-06-13-mesh_umls_mapping_en * Add model 2023-06-13-snomed_umls_mapping_en * Add model 2023-06-13-re_bodypart_directions_pipeline_en * Add model 2023-06-13-icd10_icd9_mapping_en * Add model 2023-06-13-re_bodypart_proceduretest_pipeline_en * Add model 2023-06-13-re_human_phenotype_gene_clinical_pipeline_en * Add model 2023-06-13-re_temporal_events_clinical_pipeline_en * Add model 2023-06-13-re_temporal_events_enriched_clinical_pipeline_en * Add model 2023-06-13-re_test_problem_finding_pipeline_en * Add model 2023-06-13-re_test_result_date_pipeline_en * Add model 2023-06-13-explain_clinical_doc_medication_en * Add model 2023-06-13-icdo_snomed_mapping_en * Add model 2023-06-13-rxnorm_ndc_mapping_en * Add model 2023-06-13-snomed_icdo_mapping_en * Add model 2023-06-13-oncology_general_pipeline_en * Add model 2023-06-13-oncology_biomarker_pipeline_en * Add model 2023-06-13-ner_eu_clinical_condition_pipeline_eu * Add model 2023-06-13-ner_eu_clinical_case_pipeline_en * Add model 2023-06-13-ner_eu_clinical_case_pipeline_es * Add model 2023-06-13-ner_eu_clinical_case_pipeline_eu * Add model 2023-06-13-ner_eu_clinical_case_pipeline_fr * Add model 2023-06-13-ner_eu_clinical_condition_pipeline_es * Add model 2023-06-13-ner_eu_clinical_condition_pipeline_fr * Add model 2023-06-13-ner_eu_clinical_condition_pipeline_it * Add model 2023-06-13-ner_oncology_anatomy_general_healthcare_pipeline_en * Add model 2023-06-13-ner_oncology_anatomy_general_pipeline_en * Add model 2023-06-13-ner_oncology_biomarker_healthcare_pipeline_en * Add model 2023-06-13-ner_oncology_posology_pipeline_en * Add model 2023-06-13-ner_oncology_unspecific_posology_healthcare_pipeline_en * Add model 2023-06-13-ner_sdoh_mentions_pipeline_en * Add model 2023-06-13-ner_clinical_bert_pipeline_ro * Add model 2023-06-13-ner_clinical_pipeline_ro * Add model 2023-06-13-ner_clinical_trials_abstracts_pipeline_es * Add model 2023-06-13-ner_covid_trials_pipeline_en * Add model 2023-06-13-ner_deid_generic_bert_pipeline_ro * Add model 2023-06-13-ner_deid_generic_pipeline_ro * Add model 2023-06-13-ner_deid_subentity_bert_pipeline_ro * Add model 2023-06-13-ner_deid_subentity_pipeline_ro * Add model 2023-06-13-ner_living_species_300_pipeline_es * Add model 2023-06-13-ner_negation_uncertainty_pipeline_es * Add model 2023-06-13-ner_oncology_biomarker_pipeline_en * Add model 2023-06-13-ner_oncology_demographics_pipeline_en * Add model 2023-06-13-ner_oncology_diagnosis_pipeline_en * Add model 2023-06-13-ner_oncology_response_to_treatment_pipeline_en * Add model 2023-06-13-ner_oncology_test_pipeline_en * Add model 2023-06-13-ner_oncology_therapy_pipeline_en * Add model 2023-06-13-ner_oncology_tnm_pipeline_en * Add model 2023-06-13-ner_oncology_unspecific_posology_pipeline_en * Add model 2023-06-13-ner_pharmacology_pipeline_es * Add model 2023-06-13-ner_deid_generic_glove_pipeline_en * Add model 2023-06-13-ner_deid_generic_pipeline_it * Add model 2023-06-13-ner_deid_subentity_glove_pipeline_en * Add model 2023-06-13-ner_deid_subentity_pipeline_it * Add model 2023-06-13-ner_deid_synthetic_pipeline_en * Add model 2023-06-13-ner_living_species_bert_pipeline_es * Add model 2023-06-13-ner_living_species_bert_pipeline_fr * Add model 2023-06-13-ner_living_species_bert_pipeline_it * Add model 2023-06-13-ner_living_species_bert_pipeline_pt * Add model 2023-06-13-ner_living_species_bert_pipeline_ro * Add model 2023-06-13-ner_living_species_pipeline_ca * Add model 2023-06-13-ner_living_species_pipeline_en * Add model 2023-06-13-ner_living_species_pipeline_es * Add model 2023-06-13-ner_living_species_pipeline_fr * Add model 2023-06-13-ner_living_species_pipeline_gl * Add model 2023-06-13-ner_living_species_pipeline_it * Add model 2023-06-13-ner_living_species_pipeline_pt * Add model 2023-06-13-ner_living_species_roberta_pipeline_es * Add model 2023-06-13-ner_living_species_roberta_pipeline_pt * Add model 2023-06-13-ner_chemd_clinical_pipeline_en * Add model 2023-06-13-ner_nature_nero_clinical_pipeline_en * Add model 2023-06-13-ner_supplement_clinical_pipeline_en * Add model 2023-06-13-nerdl_tumour_demo_pipeline_en * Add model 2023-06-13-ner_diag_proc_pipeline_es * Add model 2023-06-13-ner_healthcare_pipeline_de * Add model 2023-06-13-ner_healthcare_slim_pipeline_de * Add model 2023-06-13-ner_neoplasms_pipeline_es * Add model 2023-06-13-bert_token_classifier_ade_tweet_binary_pipeline_en * Add model 2023-06-13-bert_token_classifier_negation_uncertainty_pipeline_es * Add model 2023-06-13-bert_token_classifier_ner_ade_binary_pipeline_en * Add model 2023-06-13-bert_token_classifier_disease_mentions_tweet_pipeline_es * Add model 2023-06-13-bert_token_classifier_ner_anatem_pipeline_en * Add model 2023-06-13-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en * Add model 2023-06-13-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en * Add model 2023-06-13-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en * Add model 2023-06-13-bert_token_classifier_ner_living_species_pipeline_es * Add model 2023-06-13-bert_token_classifier_ner_living_species_pipeline_en * Add model 2023-06-13-bert_token_classifier_ner_bc2gm_gene_pipeline_en * Add model 2023-06-13-bert_token_classifier_ner_linnaeus_species_pipeline_en * Add model 2023-06-13-bert_token_classifier_ner_living_species_pipeline_pt * Add model 2023-06-13-bert_token_classifier_ner_jnlpba_cellular_pipeline_en * Add model 2023-06-13-ner_living_species_biobert_pipeline_en * Add model 2023-06-13-bert_token_classifier_pharmacology_pipeline_es * Add model 2023-06-13-bert_token_classifier_ner_species_pipeline_en * Add model 2023-06-13-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es * Add model 2023-06-13-bert_token_classifier_ner_bc5cdr_disease_pipeline_en * Add model 2023-06-13-bert_token_classifier_ner_living_species_pipeline_it * Add model 2023-06-13-bert_token_classifier_ner_pathogen_pipeline_en * Add model 2023-06-13-bert_token_classifier_ner_ncbi_disease_pipeline_en --------- Co-authored-by: ahmedlone127 * 2023-06-13-ner_profiling_biobert_en (#375) * Add model 2023-06-13-ner_profiling_biobert_en * Add model 2023-06-13-snomed_icd10cm_mapping_en * Add model 2023-06-13-recognize_entities_posology_en * Add model 2023-06-13-icd10cm_umls_mapping_en * Add model 2023-06-13-mesh_umls_mapping_en * Add model 2023-06-13-clinical_deidentification_de * Add model 2023-06-13-rxnorm_mesh_mapping_en * Add model 2023-06-13-clinical_deidentification_augmented_es * Add model 2023-06-13-clinical_deidentification_es * Add model 2023-06-13-clinical_deidentification_glove_en * Add model 2023-06-13-clinical_deidentification_fr * Add model 2023-06-13-ner_deid_generic_augmented_pipeline_en * Add model 2023-06-13-snomed_umls_mapping_en * Add model 2023-06-13-rxnorm_umls_mapping_en * Add model 2023-06-13-icd10_icd9_mapping_en * Add model 2023-06-13-bert_token_classifier_ner_jsl_pipeline_en * Add model 2023-06-13-clinical_deidentification_it * Add model 2023-06-13-re_bodypart_directions_pipeline_en * Add model 2023-06-13-re_bodypart_proceduretest_pipeline_en * Add model 2023-06-13-re_human_phenotype_gene_clinical_pipeline_en * Add model 2023-06-13-re_temporal_events_clinical_pipeline_en * Add model 2023-06-13-re_temporal_events_enriched_clinical_pipeline_en * Add model 2023-06-13-re_test_problem_finding_pipeline_en * Add model 2023-06-13-re_test_result_date_pipeline_en * Add model 2023-06-13-clinical_deidentification_pt * Add model 2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en * Add model 2023-06-13-icd10cm_snomed_mapping_en * Add model 2023-06-13-icdo_snomed_mapping_en * Add model 2023-06-13-rxnorm_ndc_mapping_en * Add model 2023-06-13-clinical_deidentification_ro * Add model 2023-06-13-clinical_deidentification_slim_en * Add model 2023-06-13-ner_medication_pipeline_en * Add model 2023-06-13-snomed_icdo_mapping_en * Add model 2023-06-13-ner_deid_subentity_pipeline_ar --------- Co-authored-by: C-K-Loan * 2023-06-13-bert_sequence_classifier_vop_drug_side_effect_en (#376) * Add model 2023-06-14-clinical_deidentification_ar (#382) * 2023-06-13-recognize_entities_posology_en (#378) * Add model 2023-06-13-recognize_entities_posology_en * Add model 2023-06-13-snomed_icd10cm_mapping_en * Add model 2023-06-13-icd10cm_umls_mapping_en * Add model 2023-06-13-mesh_umls_mapping_en * Add model 2023-06-13-clinical_deidentification_de * Add model 2023-06-13-clinical_deidentification_es * Add model 2023-06-13-snomed_umls_mapping_en * Add model 2023-06-13-rxnorm_umls_mapping_en * Add model 2023-06-13-clinical_deidentification_glove_en * Add model 2023-06-13-ner_deid_generic_augmented_pipeline_en * Add model 2023-06-13-clinical_deidentification_fr * Add model 2023-06-13-icd10_icd9_mapping_en * Add model 2023-06-13-bert_token_classifier_ner_jsl_pipeline_en * Add model 2023-06-13-clinical_deidentification_augmented_es * Add model 2023-06-13-rxnorm_mesh_mapping_en * Add model 2023-06-13-re_bodypart_directions_pipeline_en * Add model 2023-06-13-clinical_deidentification_it * Add model 2023-06-13-re_bodypart_proceduretest_pipeline_en * Add model 2023-06-13-re_human_phenotype_gene_clinical_pipeline_en * Add model 2023-06-13-re_temporal_events_clinical_pipeline_en * Add model 2023-06-13-re_temporal_events_enriched_clinical_pipeline_en * Add model 2023-06-13-re_test_problem_finding_pipeline_en * Add model 2023-06-13-re_test_result_date_pipeline_en * Add model 2023-06-13-clinical_deidentification_pt * Add model 2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en * Add model 2023-06-13-icd10cm_snomed_mapping_en * Add model 2023-06-13-icdo_snomed_mapping_en * Add model 2023-06-13-rxnorm_ndc_mapping_en * Add model 2023-06-13-clinical_deidentification_ro * Add model 2023-06-13-clinical_deidentification_slim_en * Add model 2023-06-13-ner_medication_pipeline_en * Add model 2023-06-13-snomed_icdo_mapping_en * Add model 2023-06-13-ner_deid_subentity_pipeline_ar --------- Co-authored-by: Cabir40 * 2023-06-13-bert_sequence_classifier_vop_side_effect_pipeline_en (#380) --------- Co-authored-by: jsl-models <74001263+jsl-models@users.noreply.github.com> Co-authored-by: Ahmetemintek Co-authored-by: Cabir40 Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com> Co-authored-by: Damla-Gurbaz Co-authored-by: Veysel Kocaman Co-authored-by: mauro-nievoff Co-authored-by: Damla Gurbaz <81505007+Damla-Gurbaz@users.noreply.github.com> Co-authored-by: hsaglamlar Co-authored-by: Cabir ÇELİK Co-authored-by: SKocer Co-authored-by: Meryem1425 Co-authored-by: HashamUlHaq Co-authored-by: Halil Saglamlar <47859156+hsaglamlar@users.noreply.github.com> Co-authored-by: andrei9825 Co-authored-by: gpirge Co-authored-by: gursev.pirge <67619330+gpirge@users.noreply.github.com> Co-authored-by: Samed K <110497137+SKocer@users.noreply.github.com> Co-authored-by: muhammetsnts <76607915+muhammetsnts@users.noreply.github.com> Co-authored-by: mauro-nievoff <55700369+mauro-nievoff@users.noreply.github.com> Co-authored-by: mellahysf Co-authored-by: dcecchini Co-authored-by: ahmedlone127 Co-authored-by: C-K-Loan --- ...assifier_binary_rct_biobert_pipeline_en.md | 120 +++++ ...rt_token_classifier_ner_jsl_pipeline_en.md | 150 +++++++ ...-clinical_deidentification_augmented_es.md | 422 +++++++++++++++++ ...2023-06-13-clinical_deidentification_de.md | 253 +++++++++++ ...2023-06-13-clinical_deidentification_es.md | 425 ++++++++++++++++++ ...2023-06-13-clinical_deidentification_fr.md | 350 +++++++++++++++ ...6-13-clinical_deidentification_glove_en.md | 197 ++++++++ ...2023-06-13-clinical_deidentification_it.md | 347 ++++++++++++++ ...2023-06-13-clinical_deidentification_pt.md | 396 ++++++++++++++++ ...2023-06-13-clinical_deidentification_ro.md | 191 ++++++++ ...06-13-clinical_deidentification_slim_en.md | 212 +++++++++ .../2023-06-13-icd10_icd9_mapping_en.md | 118 +++++ .../2023-06-13-icd10cm_snomed_mapping_en.md | 118 +++++ .../2023-06-13-icd10cm_umls_mapping_en.md | 117 +++++ .../2023-06-13-icdo_snomed_mapping_en.md | 118 +++++ .../2023-06-13-mesh_umls_mapping_en.md | 118 +++++ ...-ner_deid_generic_augmented_pipeline_en.md | 127 ++++++ ...23-06-13-ner_deid_subentity_pipeline_ar.md | 133 ++++++ .../2023-06-13-ner_medication_pipeline_en.md | 122 +++++ ...6-13-re_bodypart_directions_pipeline_en.md | 133 ++++++ ...3-re_bodypart_proceduretest_pipeline_en.md | 125 ++++++ ...man_phenotype_gene_clinical_pipeline_en.md | 122 +++++ ...re_temporal_events_clinical_pipeline_en.md | 122 +++++ ...al_events_enriched_clinical_pipeline_en.md | 122 +++++ ...-13-re_test_problem_finding_pipeline_en.md | 125 ++++++ ...3-06-13-re_test_result_date_pipeline_en.md | 127 ++++++ ...23-06-13-recognize_entities_posology_en.md | 133 ++++++ .../2023-06-13-rxnorm_mesh_mapping_en.md | 123 +++++ .../2023-06-13-rxnorm_ndc_mapping_en.md | 120 +++++ .../2023-06-13-rxnorm_umls_mapping_en.md | 118 +++++ .../2023-06-13-snomed_icd10cm_mapping_en.md | 118 +++++ .../2023-06-13-snomed_icdo_mapping_en.md | 118 +++++ .../2023-06-13-snomed_umls_mapping_en.md | 118 +++++ ..._classifier_vop_side_effect_pipeline_en.md | 76 ++++ ...sifier_vop_drug_side_effect_pipeline_en.md | 77 ++++ ..._classifier_vop_hcp_consult_pipeline_en.md | 77 ++++ ..._classifier_vop_self_report_pipeline_en.md | 77 ++++ ...lassifier_vop_sound_medical_pipeline_en.md | 76 ++++ 38 files changed, 6141 insertions(+) create mode 100644 docs/_posts/Cabir40/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md create mode 100644 docs/_posts/Cabir40/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md create mode 100644 docs/_posts/Cabir40/2023-06-13-clinical_deidentification_augmented_es.md create mode 100644 docs/_posts/Cabir40/2023-06-13-clinical_deidentification_de.md create mode 100644 docs/_posts/Cabir40/2023-06-13-clinical_deidentification_es.md create mode 100644 docs/_posts/Cabir40/2023-06-13-clinical_deidentification_fr.md create mode 100644 docs/_posts/Cabir40/2023-06-13-clinical_deidentification_glove_en.md create mode 100644 docs/_posts/Cabir40/2023-06-13-clinical_deidentification_it.md create mode 100644 docs/_posts/Cabir40/2023-06-13-clinical_deidentification_pt.md create mode 100644 docs/_posts/Cabir40/2023-06-13-clinical_deidentification_ro.md create mode 100644 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docs/_posts/mauro-nievoff/2023-06-13-bert_sequence_classifier_vop_side_effect_pipeline_en.md create mode 100644 docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_drug_side_effect_pipeline_en.md create mode 100644 docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md create mode 100644 docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_self_report_pipeline_en.md create mode 100644 docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_sound_medical_pipeline_en.md diff --git a/docs/_posts/Cabir40/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md new file mode 100644 index 0000000000..cdef8b618c --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: RCT Binary Classifier (BioBERT) Pipeline +author: John Snow Labs +name: bert_sequence_classifier_binary_rct_biobert_pipeline +date: 2023-06-13 +tags: [licensed, classifier, rct, clinical, en] +task: Entity Resolution +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pre-trained pipeline is a BioBERT based classifier that can classify if an article is a randomized clinical trial (RCT) or not. This pretrained pipeline is built on the top of [bert_sequence_classifier_binary_rct_biobert](https://nlp.johnsnowlabs.com/2022/04/25/bert_sequence_classifier_binary_rct_biobert_en_3_0.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_binary_rct_biobert_pipeline_en_4.4.4_3.4_1686676480591.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_binary_rct_biobert_pipeline_en_4.4.4_3.4_1686676480591.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models") + +result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models") + +val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """) +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models") + +result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models") + +val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """) +``` +
+ +## Results + +```bash +Results + + ++----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +|rct |text | ++----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +|True|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. | ++----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_binary_rct_biobert_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|406.0 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- MedicalBertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md new file mode 100644 index 0000000000..48a7af0870 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md @@ -0,0 +1,150 @@ +--- +layout: model +title: Pipeline to Detect Clinical Entities (BertForTokenClassifier) +author: John Snow Labs +name: bert_token_classifier_ner_jsl_pipeline +date: 2023-06-13 +tags: [ner_jsl, ner, berfortokenclassification, en, licensed] +task: Named Entity Recognition +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl](https://nlp.johnsnowlabs.com/2022/03/21/bert_token_classifier_ner_jsl_en_2_4.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/bert_token_classifier_ner_jsl_pipeline_en_4.4.4_3.4_1686675054357.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/bert_token_classifier_ner_jsl_pipeline_en_4.4.4_3.4_1686675054357.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models") + +text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.''' + +result = pipeline.fullAnnotate(text) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models") + +val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature." + +val result = pipeline.fullAnnotate(text) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models") + +text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.''' + +result = pipeline.fullAnnotate(text) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models") + +val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature." + +val result = pipeline.fullAnnotate(text) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""") +``` +
+ +## Results + +```bash +Results + + +| | ner_chunk | begin | end | ner_label | confidence | +|---:|:---------------------------------|--------:|------:|:-------------|-------------:| +| 0 | 21-day-old | 17 | 26 | Age | 0.999456 | +| 1 | Caucasian male | 28 | 41 | Demographics | 0.9901 | +| 2 | congestion | 62 | 71 | Symptom | 0.997918 | +| 3 | mom | 75 | 77 | Demographics | 0.999013 | +| 4 | yellow discharge | 99 | 114 | Symptom | 0.998663 | +| 5 | nares | 135 | 139 | Body_Part | 0.998609 | +| 6 | she | 147 | 149 | Demographics | 0.999442 | +| 7 | mild problems with his breathing | 168 | 199 | Symptom | 0.930385 | +| 8 | perioral cyanosis | 237 | 253 | Symptom | 0.99819 | +| 9 | retractions | 258 | 268 | Symptom | 0.999783 | +| 10 | One day ago | 272 | 282 | Date_Time | 0.999386 | +| 11 | mom | 285 | 287 | Demographics | 0.999835 | +| 12 | tactile temperature | 304 | 322 | Symptom | 0.999352 | +| 13 | Tylenol | 345 | 351 | Drug | 0.999762 | +| 14 | Baby-girl | 354 | 362 | Age | 0.980529 | +| 15 | decreased p.o. intake | 382 | 402 | Symptom | 0.998978 | +| 16 | His | 405 | 407 | Demographics | 0.999913 | +| 17 | breast-feeding | 416 | 429 | Body_Part | 0.99954 | +| 18 | his | 493 | 495 | Demographics | 0.999661 | +| 19 | respiratory congestion | 497 | 518 | Symptom | 0.834984 | +| 20 | He | 521 | 522 | Demographics | 0.999858 | +| 21 | tired | 555 | 559 | Symptom | 0.999516 | +| 22 | fussy | 574 | 578 | Symptom | 0.997592 | +| 23 | over the past 2 days | 580 | 599 | Date_Time | 0.994786 | +| 24 | albuterol | 642 | 650 | Drug | 0.999735 | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_ner_jsl_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|405.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- MedicalBertForTokenClassifier +- NerConverterInternalModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_augmented_es.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_augmented_es.md new file mode 100644 index 0000000000..6eb9ef93a1 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_augmented_es.md @@ -0,0 +1,422 @@ +--- +layout: model +title: Clinical Deidentification (Spanish, augmented) +author: John Snow Labs +name: clinical_deidentification_augmented +date: 2023-06-13 +tags: [deid, es, licensed] +task: De-identification +language: es +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pipeline is trained with sciwiki_300d embeddings and can be used to deidentify PHI information from medical texts in Spanish. It differs from the previous `clinical_deidentificaiton` pipeline in that it includes the `ner_deid_subentity_augmented` NER model and some improvements in ContextualParsers and RegexMatchers. + +The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask, fake or obfuscate the following entities: `AGE`, `DATE`, `PROFESSION`, `EMAIL`, `USERNAME`, `STREET`, `COUNTRY`, `CITY`, `DOCTOR`, `HOSPITAL`, `PATIENT`, `URL`, `MEDICALRECORD`, `IDNUM`, `ORGANIZATION`, `PHONE`, `ZIP`, `ACCOUNT`, `SSN`, `PLATE`, `SEX` and `IPADDR` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_augmented_es_4.4.4_3.4_1686674704829.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_augmented_es_4.4.4_3.4_1686674704829.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from johnsnowlabs import * + +deid_pipeline = PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models") + +sample = """Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""" + +result = deid_pipeline .annotate(sample) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline +val deid_pipeline = new PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models") + +sample = "Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com +" + +val result = deid_pipeline.annotate(sample) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.deid.clinical_augmented").predict("""Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from johnsnowlabs import * + +deid_pipeline = PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models") + +sample = """Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""" + +result = deid_pipeline .annotate(sample) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline +val deid_pipeline = new PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models") + +sample = "Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com +" + +val result = deid_pipeline.annotate(sample) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.deid.clinical_augmented").predict("""Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""") +``` +
+ +## Results + +```bash +Results + + +Masked with entity labels +------------------------------ +Datos . +Nombre: . +Apellidos: . +NHC: . +NASS: . +Domicilio: , B.. +Localidad/ Provincia: . +CP: . +Datos asistenciales. +Fecha de nacimiento: . +País: . +Edad: años Sexo: . +Fecha de Ingreso: . +Médico: NºCol: . +Informe clínico : de años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. +Antes de comenzar el cuadro estuvo en en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. +Entre los comensales aparecieron varios casos de brucelosis. +Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. +En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. +Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. +Auscultación pulmonar con conservación del murmullo vesicular. +Abdomen blando, depresible, sin masas ni megalias. +En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. +Extremidades sin varices ni edemas. +Pulsos periféricos presentes y simétricos. +En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. +VSG: 40 mm 1ª hora. +Coagulación: TQ 87%; + 25,8 seg. +Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. +Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: +++; +Test de Coombs > ; Brucellacapt > 1/5120. +Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). + significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. + Servicio de Endocrinología y Nutrición - () Correo electrónico: + +Masked with chars +------------------------------ +Datos [**********]. +Nombre: [**] . +Apellidos: [*************]. +NHC: [*****]. +NASS: [************]. +Domicilio: [*******************], * B.. +Localidad/ Provincia: [****]. +CP: [***]. +Datos asistenciales. +Fecha de nacimiento: [********]. +País: [****]. +Edad: ** años Sexo: *. +Fecha de Ingreso: [********]. +Médico: [******************] NºCol: [*********]. +Informe clínico [**********]: [***] de ** años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. +Antes de comenzar el cuadro estuvo en [*********] en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. +Entre los comensales aparecieron varios casos de brucelosis. +Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. +En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. +Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. +Auscultación pulmonar con conservación del murmullo vesicular. +Abdomen blando, depresible, sin masas ni megalias. +En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. +Extremidades sin varices ni edemas. +Pulsos periféricos presentes y simétricos. +En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. +VSG: 40 mm 1ª hora. +Coagulación: TQ 87%; +[**] 25,8 seg. +Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. +Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: [*************] +++; +Test de Coombs > [****]; Brucellacapt > 1/5120. +Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). +[*********] [****] significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. +[******************] [******************************] Servicio de Endocrinología y Nutrición [***************************] [***] [****] - [****] ([****]) Correo electrónico: [********************] + +Masked with fixed length chars +------------------------------ +Datos ****. +Nombre: **** . +Apellidos: ****. +NHC: ****. +NASS: ****. +Domicilio: ****, **** B.. +Localidad/ Provincia: ****. +CP: ****. +Datos asistenciales. +Fecha de nacimiento: ****. +País: ****. +Edad: **** años Sexo: ****. +Fecha de Ingreso: ****. +Médico: **** NºCol: ****. +Informe clínico ****: **** de **** años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. +Antes de comenzar el cuadro estuvo en **** en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. +Entre los comensales aparecieron varios casos de brucelosis. +Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. +En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. +Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. +Auscultación pulmonar con conservación del murmullo vesicular. +Abdomen blando, depresible, sin masas ni megalias. +En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. +Extremidades sin varices ni edemas. +Pulsos periféricos presentes y simétricos. +En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. +VSG: 40 mm 1ª hora. +Coagulación: TQ 87%; +**** 25,8 seg. +Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. +Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: **** +++; +Test de Coombs > ****; Brucellacapt > 1/5120. +Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). +**** **** significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. +**** **** Servicio de Endocrinología y Nutrición **** **** **** - **** (****) Correo electrónico: **** + +Obfuscated +------------------------------ +Datos Hombre. +Nombre: Aurora Garrido Paez . +Apellidos: Aurora Garrido Paez. +NHC: BBBBBBBBQR648597. +NASS: 48127833R. +Domicilio: C/ Rambla, 246, 5 B.. +Localidad/ Provincia: Alicante. +CP: 24202. +Datos asistenciales. +Fecha de nacimiento: 21/04/1977. +País: Portugal. +Edad: 56 años Sexo: Hombre. +Fecha de Ingreso: 10/07/2018. +Médico: Francisco José Roca Bermúdez NºCol: 21344083-P. +Informe clínico Hombre: 041010000011 de 56 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. +Antes de comenzar el cuadro estuvo en Zaragoza en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. +Entre los comensales aparecieron varios casos de brucelosis. +Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. +En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. +Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. +Auscultación pulmonar con conservación del murmullo vesicular. +Abdomen blando, depresible, sin masas ni megalias. +En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. +Extremidades sin varices ni edemas. +Pulsos periféricos presentes y simétricos. +En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. +VSG: 40 mm 1ª hora. +Coagulación: TQ 87%; +Tecnogroup SL 25,8 seg. +Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. +Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: María Miguélez Sanz +++; +Test de Coombs > 07-25-1974; Brucellacapt > 1/5120. +Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). +F. 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. +Francisco José Roca Bermúdez Hospital 12 de Octubre Servicio de Endocrinología y Nutrición Calle Ramón y Cajal s/n 03129 Zaragoza - Alicante (Portugal) Correo electrónico: richard@company.it + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_deidentification_augmented| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|es| +|Size:|281.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- RegexMatcherModel +- ChunkMergeModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_de.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_de.md new file mode 100644 index 0000000000..12ef24d078 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_de.md @@ -0,0 +1,253 @@ +--- +layout: model +title: Clinical Deidentification +author: John Snow Labs +name: clinical_deidentification +date: 2023-06-13 +tags: [deidentification, pipeline, de, licensed, clinical] +task: Pipeline Healthcare +language: de +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pipeline can be used to deidentify PHI information from **German** medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate `PATIENT`, `HOSPITAL`, `DATE`, `ORGANIZATION`, `CITY`, `STREET`, `USERNAME`, `PROFESSION`, `PHONE`, `COUNTRY`, `DOCTOR`, `AGE`, `CONTACT`, `ID`, `PHONE`, `ZIP`, `ACCOUNT`, `SSN`, `DLN`, `PLATE` entities. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_de_4.4.4_3.4_1686674620606.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_de_4.4.4_3.4_1686674620606.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "de", "clinical/models") + +sample = """Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. +Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen. + +Persönliche Daten : +ID-Nummer: T0110053F +Platte A-BC124 +Kontonummer: DE89370400440532013000 +SSN : 13110587M565 +Lizenznummer: B072RRE2I55 +Adresse : St.Johann-Straße 13 19300 +""" + +result = deid_pipeline.annotate(sample) +print("\n".join(result['masked'])) +print("\n".join(result['masked_with_chars'])) +print("\n".join(result['masked_fixed_length_chars'])) +print("\n".join(result['obfuscated'])) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val deid_pipeline = PretrainedPipeline("clinical_deidentification","de","clinical/models") + +val sample = "Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. +Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen. + +Persönliche Daten : +ID-Nummer: T0110053F +Platte A-BC124 +Kontonummer: DE89370400440532013000 +SSN : 13110587M565 +Lizenznummer: B072RRE2I55 +Adresse : St.Johann-Straße 13 19300" + +val result = deid_pipeline.annotate(sample) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.deid.clinical").predict("""Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. +Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen. + +Persönliche Daten : +ID-Nummer: T0110053F +Platte A-BC124 +Kontonummer: DE89370400440532013000 +SSN : 13110587M565 +Lizenznummer: B072RRE2I55 +Adresse : St.Johann-Straße 13 19300 +""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "de", "clinical/models") + +sample = """Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. +Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen. + +Persönliche Daten : +ID-Nummer: T0110053F +Platte A-BC124 +Kontonummer: DE89370400440532013000 +SSN : 13110587M565 +Lizenznummer: B072RRE2I55 +Adresse : St.Johann-Straße 13 19300 +""" + +result = deid_pipeline.annotate(sample) +print("\n".join(result['masked'])) +print("\n".join(result['masked_with_chars'])) +print("\n".join(result['masked_fixed_length_chars'])) +print("\n".join(result['obfuscated'])) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val deid_pipeline = PretrainedPipeline("clinical_deidentification","de","clinical/models") + +val sample = "Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. +Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen. + +Persönliche Daten : +ID-Nummer: T0110053F +Platte A-BC124 +Kontonummer: DE89370400440532013000 +SSN : 13110587M565 +Lizenznummer: B072RRE2I55 +Adresse : St.Johann-Straße 13 19300" + +val result = deid_pipeline.annotate(sample) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.deid.clinical").predict("""Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. +Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen. + +Persönliche Daten : +ID-Nummer: T0110053F +Platte A-BC124 +Kontonummer: DE89370400440532013000 +SSN : 13110587M565 +Lizenznummer: B072RRE2I55 +Adresse : St.Johann-Straße 13 19300 +""") +``` +
+ +## Results + +```bash +Results + + +Masked with entity labels +------------------------------ +Zusammenfassung : wird am Morgen des ins eingeliefert. +Herr ist Jahre alt und hat zu viel Wasser in den Beinen. +Persönliche Daten : +ID-Nummer: +Platte +Kontonummer: +SSN : +Lizenznummer: +Adresse : + +Masked with chars +------------------------------ +Zusammenfassung : [************] wird am Morgen des [**************] ins [**********************] eingeliefert. +Herr [************] ist ** Jahre alt und hat zu viel Wasser in den Beinen. +Persönliche Daten : +ID-Nummer: [*******] +Platte [*****] +Kontonummer: [********************] +SSN : [**********] +Lizenznummer: [*********] +Adresse : [*****************] [***] + +Masked with fixed length chars +------------------------------ +Zusammenfassung : **** wird am Morgen des **** ins **** eingeliefert. +Herr **** ist **** Jahre alt und hat zu viel Wasser in den Beinen. +Persönliche Daten : +ID-Nummer: **** +Platte **** +Kontonummer: **** +SSN : **** +Lizenznummer: **** +Adresse : **** **** + +Obfusceted +------------------------------ +Zusammenfassung : Herrmann Kallert wird am Morgen des 11-26-1977 ins International Neuroscience eingeliefert. +Herr Herrmann Kallert ist 79 Jahre alt und hat zu viel Wasser in den Beinen. +Persönliche Daten : +ID-Nummer: 136704D357 +Platte QA348G +Kontonummer: 192837465738 +SSN : 1310011981M454 +Lizenznummer: XX123456 +Adresse : Klingelhöferring 31206 + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_deidentification| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|de| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ChunkMergeModel +- ChunkMergeModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_es.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_es.md new file mode 100644 index 0000000000..af4d2b1216 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_es.md @@ -0,0 +1,425 @@ +--- +layout: model +title: Clinical Deidentification (Spanish) +author: John Snow Labs +name: clinical_deidentification +date: 2023-06-13 +tags: [deid, es, licensed] +task: De-identification +language: es +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pipeline is trained with sciwiki_300d embeddings and can be used to deidentify PHI information from medical texts in Spanish. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask, fake or obfuscate the following entities: `AGE`, `DATE`, `PROFESSION`, `E-MAIL`, `USERNAME`, `LOCATION`, `DOCTOR`, `HOSPITAL`, `PATIENT`, `URL`, `IP`, `MEDICALRECORD`, `IDNUM`, `ORGANIZATION`, `PHONE`, `ZIP`, `ACCOUNT`, `SSN`, `PLATE`, `SEX` and `IPADDR` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_es_4.4.4_3.4_1686674678630.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_es_4.4.4_3.4_1686674678630.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from johnsnowlabs import * + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "es", "clinical/models") + +sample = """Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com +""" + +result = deid_pipeline .annotate(sample) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline +val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "es", "clinical/models") + +sample = "Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com +" + +val result = deid_pipeline.annotate(sample) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.deid.clinical").predict("""Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com +""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from johnsnowlabs import * + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "es", "clinical/models") + +sample = """Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com +""" + +result = deid_pipeline .annotate(sample) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline +val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "es", "clinical/models") + +sample = "Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com +" + +val result = deid_pipeline.annotate(sample) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.deid.clinical").predict("""Datos del paciente. +Nombre: Jose . +Apellidos: Aranda Martinez. +NHC: 2748903. +NASS: 26 37482910 04. +Domicilio: Calle Losada Martí 23, 5 B.. +Localidad/ Provincia: Madrid. +CP: 28016. +Datos asistenciales. +Fecha de nacimiento: 15/04/1977. +País: España. +Edad: 37 años Sexo: F. +Fecha de Ingreso: 05/06/2018. +Médico: María Merino Viveros NºCol: 28 28 35489. +Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com +""") +``` +
+ +## Results + +```bash +Results + + +Masked with entity labels +------------------------------ +Datos del paciente. +Nombre: . +Apellidos: . +NHC: . +NASS: 04. +Domicilio: , 5 B.. +Localidad/ Provincia: . +CP: . +Datos asistenciales. +Fecha de nacimiento: . +País: . +Edad: años Sexo: . +Fecha de Ingreso: . +: María Merino Viveros NºCol: . +Informe clínico del paciente: de años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. +Antes de comenzar el cuadro estuvo en en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. +Entre los comensales aparecieron varios casos de brucelosis. +Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. +En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. +Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. +Auscultación pulmonar con conservación del murmullo vesicular. +Abdomen blando, depresible, sin masas ni megalias. +En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. +Extremidades sin varices ni edemas. +Pulsos periféricos presentes y simétricos. +En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. +VSG: 40 mm 1ª hora. +Coagulación: TQ 87%; +TTPA 25,8 seg. +Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. +Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: +++; +Test de Coombs > 1/1280; Brucellacapt > 1/5120. +Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). +El paciente significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. + Servicio de Endocrinología y Nutrición km 12,500 28905 - () Correo electrónico: + +Masked with chars +------------------------------ +Datos del paciente. +Nombre: [**] . +Apellidos: [*************]. +NHC: [*****]. +NASS: ** [******] 04. +Domicilio: [*******************], 5 B.. +Localidad/ Provincia: [****]. +CP: [***]. +Datos asistenciales. +Fecha de nacimiento: [********]. +País: [****]. +Edad: ** años Sexo: *. +Fecha de Ingreso: [********]. +[****]: María Merino Viveros NºCol: ** ** [***]. +Informe clínico del paciente: [***] de ** años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. +Antes de comenzar el cuadro estuvo en [*********] en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. +Entre los comensales aparecieron varios casos de brucelosis. +Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. +En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. +Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. +Auscultación pulmonar con conservación del murmullo vesicular. +Abdomen blando, depresible, sin masas ni megalias. +En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. +Extremidades sin varices ni edemas. +Pulsos periféricos presentes y simétricos. +En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. +VSG: 40 mm 1ª hora. +Coagulación: TQ 87%; +TTPA 25,8 seg. +Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. +Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: [*************] +++; +Test de Coombs > 1/1280; Brucellacapt > 1/5120. +Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). +El paciente [****] significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. +[******************] [******************************] Servicio de Endocrinología y Nutrición [*****************] km 12,500 28905 [****] - [****] ([****]) Correo electrónico: [********************] + +Masked with fixed length chars +------------------------------ +Datos del paciente. +Nombre: **** . +Apellidos: ****. +NHC: ****. +NASS: **** **** 04. +Domicilio: ****, 5 B.. +Localidad/ Provincia: ****. +CP: ****. +Datos asistenciales. +Fecha de nacimiento: ****. +País: ****. +Edad: **** años Sexo: ****. +Fecha de Ingreso: ****. +****: María Merino Viveros NºCol: **** **** ****. +Informe clínico del paciente: **** de **** años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. +Antes de comenzar el cuadro estuvo en **** en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. +Entre los comensales aparecieron varios casos de brucelosis. +Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. +En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. +Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. +Auscultación pulmonar con conservación del murmullo vesicular. +Abdomen blando, depresible, sin masas ni megalias. +En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. +Extremidades sin varices ni edemas. +Pulsos periféricos presentes y simétricos. +En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. +VSG: 40 mm 1ª hora. +Coagulación: TQ 87%; +TTPA 25,8 seg. +Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. +Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: **** +++; +Test de Coombs > 1/1280; Brucellacapt > 1/5120. +Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). +El paciente **** significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. +**** **** Servicio de Endocrinología y Nutrición **** km 12,500 28905 **** - **** (****) Correo electrónico: **** + +Obfuscated +------------------------------ +Datos del paciente. +Nombre: Sr. Lerma . +Apellidos: Aristides Gonzalez Gelabert. +NHC: BBBBBBBBQR648597. +NASS: 041010000011 RZRM020101906017 04. +Domicilio: Valencia, 5 B.. +Localidad/ Provincia: Madrid. +CP: 99335. +Datos asistenciales. +Fecha de nacimiento: 25/04/1977. +País: Barcelona. +Edad: 8 años Sexo: F.. +Fecha de Ingreso: 02/08/2018. +transportista: María Merino Viveros NºCol: olegario10 olegario10 felisa78. +Informe clínico del paciente: RZRM020101906017 de 8 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. +Antes de comenzar el cuadro estuvo en Madrid en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. +Entre los comensales aparecieron varios casos de brucelosis. +Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha. +La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. +En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. +Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. +Auscultación pulmonar con conservación del murmullo vesicular. +Abdomen blando, depresible, sin masas ni megalias. +En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. +Extremidades sin varices ni edemas. +Pulsos periféricos presentes y simétricos. +En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva. +Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. +VSG: 40 mm 1ª hora. +Coagulación: TQ 87%; +TTPA 25,8 seg. +Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. +Orina: sedimento normal. +Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Dra. Laguna +++; +Test de Coombs > 1/1280; Brucellacapt > 1/5120. +Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos. +Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). +El paciente 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro. +Remitido por: Dra. +Reinaldo Manjón Malo Barcelona Servicio de Endocrinología y Nutrición Valencia km 12,500 28905 Bilbao - Madrid (Barcelona) Correo electrónico: quintanasalome@example.net + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_deidentification| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|es| +|Size:|281.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ChunkMergeModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_fr.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_fr.md new file mode 100644 index 0000000000..4dff18cf89 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_fr.md @@ -0,0 +1,350 @@ +--- +layout: model +title: Clinical Deidentification (French) +author: John Snow Labs +name: clinical_deidentification +date: 2023-06-13 +tags: [deid, fr, licensed] +task: De-identification +language: fr +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pipeline can be used to deidentify PHI information from medical texts in French. The pipeline can mask and obfuscate the following entities: `DATE`, `AGE`, `SEX`, `PROFESSION`, `ORGANIZATION`, `PHONE`, `E-MAIL`, `ZIP`, `STREET`, `CITY`, `COUNTRY`, `PATIENT`, `DOCTOR`, `HOSPITAL`, `MEDICALRECORD`, `SSN`, `IDNUM`, `ACCOUNT`, `PLATE`, `USERNAME`, `URL`, and `IPADDR`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_fr_4.4.4_3.4_1686674847712.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_fr_4.4.4_3.4_1686674847712.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "fr", "clinical/models") + +sample = """COMPTE-RENDU D'HOSPITALISATION +PRENOM : Jean +NOM : Dubois +NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058 +ADRESSE : 18 Avenue Matabiau +VILLE : Grenoble +CODE POSTAL : 38000 +DATE DE NAISSANCE : 03/03/1946 +Âge : 70 ans +Sexe : H +COURRIEL : jdubois@hotmail.fr +DATE D'ADMISSION : 12/12/2016 +MÉDÉCIN : Dr Michel Renaud +RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour. +Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale. +L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV. +L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal. +Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml. +ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble +COURRIEL : mariebreton@chb.fr +""" + +result = deid_pipeline.annotate(sample) +``` +```scala +val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "fr", "clinical/models") + +sample = "COMPTE-RENDU D'HOSPITALISATION +PRENOM : Jean +NOM : Dubois +NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058 +ADRESSE : 18 Avenue Matabiau +VILLE : Grenoble +CODE POSTAL : 38000 +DATE DE NAISSANCE : 03/03/1946 +Âge : 70 ans +Sexe : H +COURRIEL : jdubois@hotmail.fr +DATE D'ADMISSION : 12/12/2016 +MÉDÉCIN : Dr Michel Renaud +RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour. +Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale. +L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV. +L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal. +Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml. +ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble +COURRIEL : mariebreton@chb.fr +" + +val result = deid_pipeline.annotate(sample) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("fr.deid_obfuscated").predict("""COMPTE-RENDU D'HOSPITALISATION +PRENOM : Jean +NOM : Dubois +NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058 +ADRESSE : 18 Avenue Matabiau +VILLE : Grenoble +CODE POSTAL : 38000 +DATE DE NAISSANCE : 03/03/1946 +Âge : 70 ans +Sexe : H +COURRIEL : jdubois@hotmail.fr +DATE D'ADMISSION : 12/12/2016 +MÉDÉCIN : Dr Michel Renaud +RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour. +Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale. +L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV. +L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal. +Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml. +ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble +COURRIEL : mariebreton@chb.fr +""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "fr", "clinical/models") + +sample = """COMPTE-RENDU D'HOSPITALISATION +PRENOM : Jean +NOM : Dubois +NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058 +ADRESSE : 18 Avenue Matabiau +VILLE : Grenoble +CODE POSTAL : 38000 +DATE DE NAISSANCE : 03/03/1946 +Âge : 70 ans +Sexe : H +COURRIEL : jdubois@hotmail.fr +DATE D'ADMISSION : 12/12/2016 +MÉDÉCIN : Dr Michel Renaud +RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour. +Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale. +L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV. +L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal. +Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml. +ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble +COURRIEL : mariebreton@chb.fr +""" + +result = deid_pipeline.annotate(sample) +``` +```scala +val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "fr", "clinical/models") + +sample = "COMPTE-RENDU D'HOSPITALISATION +PRENOM : Jean +NOM : Dubois +NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058 +ADRESSE : 18 Avenue Matabiau +VILLE : Grenoble +CODE POSTAL : 38000 +DATE DE NAISSANCE : 03/03/1946 +Âge : 70 ans +Sexe : H +COURRIEL : jdubois@hotmail.fr +DATE D'ADMISSION : 12/12/2016 +MÉDÉCIN : Dr Michel Renaud +RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour. +Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale. +L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV. +L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal. +Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml. +ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble +COURRIEL : mariebreton@chb.fr +" + +val result = deid_pipeline.annotate(sample) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.deid_obfuscated").predict("""COMPTE-RENDU D'HOSPITALISATION +PRENOM : Jean +NOM : Dubois +NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058 +ADRESSE : 18 Avenue Matabiau +VILLE : Grenoble +CODE POSTAL : 38000 +DATE DE NAISSANCE : 03/03/1946 +Âge : 70 ans +Sexe : H +COURRIEL : jdubois@hotmail.fr +DATE D'ADMISSION : 12/12/2016 +MÉDÉCIN : Dr Michel Renaud +RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour. +Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale. +L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV. +L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal. +Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml. +ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble +COURRIEL : mariebreton@chb.fr +""") +``` +
+ +## Results + +```bash +Results + + +Masked with entity labels +------------------------------ +COMPTE-RENDU D'HOSPITALISATION +PRENOM : +NOM : +NUMÉRO DE SÉCURITÉ SOCIALE : +ADRESSE : +VILLE : +CODE POSTAL : +DATE DE NAISSANCE : +Âge : +Sexe : +COURRIEL : +DATE D'ADMISSION : +MÉDÉCIN : +RAPPORT CLINIQUE : ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour. + nous a été adressé car présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale. +L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV. +L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal. +Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. +PSA de 1,16 ng/ml. +ADDRESSÉ À : - Service D'Endocrinologie et de Nutrition - , +COURRIEL : + + +Masked with chars +------------------------------ +COMPTE-RENDU D'HOSPITALISATION +PRENOM : [**] +NOM : [****] +NUMÉRO DE SÉCURITÉ SOCIALE : [***********] +ADRESSE : [****************] +VILLE : [******] +CODE POSTAL : [***] +DATE DE NAISSANCE : [********] +Âge : [****] +Sexe : * +COURRIEL : [****************] +DATE D'ADMISSION : [********] +MÉDÉCIN : [**************] +RAPPORT CLINIQUE : ** ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour. +** nous a été adressé car ** présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale. +L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV. +L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal. +Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. +PSA de 1,16 ng/ml. +ADDRESSÉ À : [**************] - [****************************] Service D'Endocrinologie et de Nutrition - [******************], [***] [******] +COURRIEL : [****************] + + +Masked with fixed length chars +------------------------------ +COMPTE-RENDU D'HOSPITALISATION +PRENOM : **** +NOM : **** +NUMÉRO DE SÉCURITÉ SOCIALE : **** +ADRESSE : **** +VILLE : **** +CODE POSTAL : **** +DATE DE NAISSANCE : **** +Âge : **** +Sexe : **** +COURRIEL : **** +DATE D'ADMISSION : **** +MÉDÉCIN : **** +RAPPORT CLINIQUE : **** ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour. +**** nous a été adressé car **** présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale. +L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV. +L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal. +Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. +PSA de 1,16 ng/ml. +ADDRESSÉ À : **** - **** Service D'Endocrinologie et de Nutrition - ****, **** **** +COURRIEL : **** + + +Obfuscated +------------------------------ +COMPTE-RENDU D'HOSPITALISATION +PRENOM : Mme Ollivier +NOM : Mme Traore +NUMÉRO DE SÉCURITÉ SOCIALE : 164033818514436 +ADRESSE : 731, boulevard de Legrand +VILLE : Sainte Antoine +CODE POSTAL : 37443 +DATE DE NAISSANCE : 18/03/1946 +Âge : 46 +Sexe : Femme +COURRIEL : georgeslemonnier@live.com +DATE D'ADMISSION : 10/01/2017 +MÉDÉCIN : Pr. Manon Dupuy +RAPPORT CLINIQUE : 26 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour. +Homme nous a été adressé car Homme présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale. +L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV. +L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal. +Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. +PSA de 1,16 ng/ml. +ADDRESSÉ À : Dr Tristan-Gilbert Poulain - CENTRE HOSPITALIER D'ORTHEZ Service D'Endocrinologie et de Nutrition - 6, avenue Pages, 37443 Sainte Antoine +COURRIEL : massecatherine@bouygtel.fr + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_deidentification| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|fr| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- RegexMatcherModel +- RegexMatcherModel +- ChunkMergeModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_glove_en.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_glove_en.md new file mode 100644 index 0000000000..a39d1c9cb3 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_glove_en.md @@ -0,0 +1,197 @@ +--- +layout: model +title: Clinical Deidentification (glove) +author: John Snow Labs +name: clinical_deidentification_glove +date: 2023-06-13 +tags: [deidentification, en, licensed, pipeline] +task: Pipeline Healthcare +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pipeline is trained with lightweight `glove_100d` embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate `AGE`, `CONTACT`, `DATE`, `ID`, `LOCATION`, `NAME`, `PROFESSION`, `CITY`, `COUNTRY`, `DOCTOR`, `HOSPITAL`, `IDNUM`, `MEDICALRECORD`, `ORGANIZATION`, `PATIENT`, `PHONE`, `PROFESSION`, `STREET`, `USERNAME`, `ZIP`, `ACCOUNT`, `LICENSE`, `VIN`, `SSN`, `DLN`, `PLATE`, `IPADDR`, `EMAIL` entities. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_glove_en_4.4.4_3.4_1686674471536.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_glove_en_4.4.4_3.4_1686674471536.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification_glove", "en", "clinical/models") + + +sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. +Dr. John Green, ID: 1231511863, IP 203.120.223.13. +He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. +Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. +Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""" + +result = deid_pipeline.annotate(sample) + +print("\n".join(result['masked'])) +print("\n".join(result['masked_with_chars'])) +print("\n".join(result['masked_fixed_length_chars'])) +print("\n".join(result['obfuscated'])) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val deid_pipeline = new PretrainedPipeline("clinical_deidentification_glove","en","clinical/models") + +val result = deid_pipeline.annotate("Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. Dr. John Green, ID: 1231511863, IP 203.120.223.13. He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.deid.glove_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. +Dr. John Green, ID: 1231511863, IP 203.120.223.13. +He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. +Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. +Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification_glove", "en", "clinical/models") + + +sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. +Dr. John Green, ID: 1231511863, IP 203.120.223.13. +He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. +Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. +Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""" + +result = deid_pipeline.annotate(sample) + +print("\n".join(result['masked'])) +print("\n".join(result['masked_with_chars'])) +print("\n".join(result['masked_fixed_length_chars'])) +print("\n".join(result['obfuscated'])) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val deid_pipeline = new PretrainedPipeline("clinical_deidentification_glove","en","clinical/models") + +val result = deid_pipeline.annotate("Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. Dr. John Green, ID: 1231511863, IP 203.120.223.13. He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.deid.glove_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. +Dr. John Green, ID: 1231511863, IP 203.120.223.13. +He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. +Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. +Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""") +``` +
+ +## Results + +```bash +Results + + +Masked with entity labels +------------------------------ +Name : , Record date: , # . +Dr. , ID: , IP . +He is a male was admitted to the for cystectomy on . +Patient's VIN : , SSN , Driver's license . +Phone , , , E-MAIL: . + +Masked with chars +------------------------------ +Name : [**************], Record date: [********], # [****]. +Dr. [********], ID: [********], IP [************]. +He is a [*********] male was admitted to the [**********] for cystectomy on [******]. +Patient's VIN : [***************], SSN [**********], Driver's license [*********]. +Phone [************], [***************], [***********], E-MAIL: [*************]. + +Masked with fixed length chars +------------------------------ +Name : ****, Record date: ****, # ****. +Dr. ****, ID: ****, IP ****. +He is a **** male was admitted to the **** for cystectomy on ****. +Patient's VIN : ****, SSN ****, Driver's license ****. +Phone ****, ****, ****, E-MAIL: ****. + +Obfuscated +------------------------------ +Name : Berneta Anis, Record date: 2093-02-19, # U4660137. +Dr. Dr Worley Colonel, ID: ZJ:9570208, IP 005.005.005.005. +He is a 67 male was admitted to the ST. LUKE'S HOSPITAL AT THE VINTAGE for cystectomy on 06-02-1981. +Patient's VIN : 3CCCC22DDDD333888, SSN SSN-618-77-1042, Driver's license W693817528998. +Phone 0496 46 46 70, 3100 weston rd, Shattuck, E-MAIL: Freddi@hotmail.com. + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_deidentification_glove| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|181.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- MedicalNerModel +- NerConverter +- ChunkMergeModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ChunkMergeModel +- ChunkMergeModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_it.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_it.md new file mode 100644 index 0000000000..b8efa816b0 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_it.md @@ -0,0 +1,347 @@ +--- +layout: model +title: Clinical Deidentification (Italian) +author: John Snow Labs +name: clinical_deidentification +date: 2023-06-13 +tags: [deidentification, pipeline, it, licensed] +task: De-identification +language: it +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pipeline can be used to deidentify PHI information from medical texts in Italian. The pipeline can mask and obfuscate the following entities: `DATE`, `AGE`, `SEX`, `PROFESSION`, `ORGANIZATION`, `PHONE`, `E-MAIL`, `ZIP`, `STREET`, `CITY`, `COUNTRY`, `PATIENT`, `DOCTOR`, `HOSPITAL`, `MEDICALRECORD`, `SSN`, `IDNUM`, `ACCOUNT`, `PLATE`, `USERNAME`, `URL`, and `IPADDR`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_it_4.4.4_3.4_1686675204843.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_it_4.4.4_3.4_1686675204843.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "it", "clinical/models") + +sample = """RAPPORTO DI RICOVERO +NOME: Lodovico Fibonacci +CODICE FISCALE: MVANSK92F09W408A +INDIRIZZO: Viale Burcardo 7 +CITTÀ : Napoli +CODICE POSTALE: 80139 +DATA DI NASCITA: 03/03/1946 +ETÀ: 70 anni +SESSO: M +EMAIL: lpizzo@tim.it +DATA DI AMMISSIONE: 12/12/2016 +DOTTORE: Eva Viviani +RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno. +È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale. +L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV. +L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale. +L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml. + +INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli +EMAIL: bferrabosco@poste.it""" + +result = deid_pipeline.annotate(sample) +``` +```scala +val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "it", "clinical/models") + +sample = "RAPPORTO DI RICOVERO +NOME: Lodovico Fibonacci +CODICE FISCALE: MVANSK92F09W408A +INDIRIZZO: Viale Burcardo 7 +CITTÀ : Napoli +CODICE POSTALE: 80139 +DATA DI NASCITA: 03/03/1946 +ETÀ: 70 anni +SESSO: M +EMAIL: lpizzo@tim.it +DATA DI AMMISSIONE: 12/12/2016 +DOTTORE: Eva Viviani +RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno. +È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale. +L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV. +L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale. +L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml. + +INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli +EMAIL: bferrabosco@poste.it" + +val result = deid_pipeline.annotate(sample) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("it.deid.clinical").predict("""RAPPORTO DI RICOVERO +NOME: Lodovico Fibonacci +CODICE FISCALE: MVANSK92F09W408A +INDIRIZZO: Viale Burcardo 7 +CITTÀ : Napoli +CODICE POSTALE: 80139 +DATA DI NASCITA: 03/03/1946 +ETÀ: 70 anni +SESSO: M +EMAIL: lpizzo@tim.it +DATA DI AMMISSIONE: 12/12/2016 +DOTTORE: Eva Viviani +RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno. +È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale. +L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV. +L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale. +L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml. + +INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli +EMAIL: bferrabosco@poste.it""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "it", "clinical/models") + +sample = """RAPPORTO DI RICOVERO +NOME: Lodovico Fibonacci +CODICE FISCALE: MVANSK92F09W408A +INDIRIZZO: Viale Burcardo 7 +CITTÀ : Napoli +CODICE POSTALE: 80139 +DATA DI NASCITA: 03/03/1946 +ETÀ: 70 anni +SESSO: M +EMAIL: lpizzo@tim.it +DATA DI AMMISSIONE: 12/12/2016 +DOTTORE: Eva Viviani +RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno. +È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale. +L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV. +L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale. +L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml. + +INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli +EMAIL: bferrabosco@poste.it""" + +result = deid_pipeline.annotate(sample) +``` +```scala +val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "it", "clinical/models") + +sample = "RAPPORTO DI RICOVERO +NOME: Lodovico Fibonacci +CODICE FISCALE: MVANSK92F09W408A +INDIRIZZO: Viale Burcardo 7 +CITTÀ : Napoli +CODICE POSTALE: 80139 +DATA DI NASCITA: 03/03/1946 +ETÀ: 70 anni +SESSO: M +EMAIL: lpizzo@tim.it +DATA DI AMMISSIONE: 12/12/2016 +DOTTORE: Eva Viviani +RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno. +È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale. +L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV. +L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale. +L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml. + +INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli +EMAIL: bferrabosco@poste.it" + +val result = deid_pipeline.annotate(sample) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("it.deid.clinical").predict("""RAPPORTO DI RICOVERO +NOME: Lodovico Fibonacci +CODICE FISCALE: MVANSK92F09W408A +INDIRIZZO: Viale Burcardo 7 +CITTÀ : Napoli +CODICE POSTALE: 80139 +DATA DI NASCITA: 03/03/1946 +ETÀ: 70 anni +SESSO: M +EMAIL: lpizzo@tim.it +DATA DI AMMISSIONE: 12/12/2016 +DOTTORE: Eva Viviani +RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno. +È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale. +L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV. +L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale. +L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml. + +INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli +EMAIL: bferrabosco@poste.it""") +``` +
+ +## Results + +```bash +Results + + +Masked with entity labels +------------------------------ +RAPPORTO DI RICOVERO +NOME: +CODICE FISCALE: +INDIRIZZO: +CITTÀ : +CODICE POSTALE: +DATA DI NASCITA: +ETÀ: anni +SESSO: +EMAIL: +DATA DI AMMISSIONE: +DOTTORE: +RAPPORTO CLINICO: anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno. +È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale. +L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV. +L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale. +L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. +PSA di 1,16 ng/ml. +INDIRIZZATO A: Dott. + - , Dipartimento di Endocrinologia e Nutrizione - , +EMAIL: + + +Masked with chars +------------------------------ +RAPPORTO DI RICOVERO +NOME: [****************] +CODICE FISCALE: [**************] +INDIRIZZO: [**************] +CITTÀ : [****] +CODICE POSTALE: [***]DATA DI NASCITA: [********] +ETÀ: **anni +SESSO: * +EMAIL: [***********] +DATA DI AMMISSIONE: [********] +DOTTORE: [*********] +RAPPORTO CLINICO: **anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno. +È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale. +L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV. +L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale. +L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. +PSA di 1,16 ng/ml. +INDIRIZZATO A: Dott. +[**************] - [*****************], Dipartimento di Endocrinologia e Nutrizione - [*******************], [***] [****] +EMAIL: [******************] + + +Masked with fixed length chars +------------------------------ +RAPPORTO DI RICOVERO +NOME: **** +CODICE FISCALE: **** +INDIRIZZO: **** +CITTÀ : **** +CODICE POSTALE: ****DATA DI NASCITA: **** +ETÀ: **** anni +SESSO: **** +EMAIL: **** +DATA DI AMMISSIONE: **** +DOTTORE: **** +RAPPORTO CLINICO: **** anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno. +È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale. +L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV. +L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale. +L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. +PSA di 1,16 ng/ml. +INDIRIZZATO A: Dott. +**** - ****, Dipartimento di Endocrinologia e Nutrizione - ****, **** **** +EMAIL: **** + + +Obfuscated +------------------------------ +RAPPORTO DI RICOVERO +NOME: Scotto-Polani +CODICE FISCALE: ECI-QLN77G15L455Y +INDIRIZZO: Viale Orlando 808 +CITTÀ : Sesto Raimondo +CODICE POSTALE: 53581DATA DI NASCITA: 09/03/1946 +ETÀ: 5 anni +SESSO: U +EMAIL: HenryWatson@world.com +DATA DI AMMISSIONE: 10/01/2017 +DOTTORE: Sig. Fredo Marangoni +RAPPORTO CLINICO: 5 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno. +È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale. +L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV. +L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale. +L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. +PSA di 1,16 ng/ml. +INDIRIZZATO A: Dott. +Antonio Rusticucci - ASL 7 DI CARBONIA AZIENDA U.S.L. N. 7, Dipartimento di Endocrinologia e Nutrizione - Via Giorgio 0 Appartamento 26, 03461 Sesto Raimondo +EMAIL: murat.g@jsl.com + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_deidentification| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|it| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- ContextualParserModel +- ContextualParserModel +- RegexMatcherModel +- RegexMatcherModel +- RegexMatcherModel +- RegexMatcherModel +- RegexMatcherModel +- RegexMatcherModel +- RegexMatcherModel +- RegexMatcherModel +- RegexMatcherModel +- RegexMatcherModel +- ChunkMergeModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_pt.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_pt.md new file mode 100644 index 0000000000..53eac71b56 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_pt.md @@ -0,0 +1,396 @@ +--- +layout: model +title: Clinical Deidentification Pipeline (Portuguese) +author: John Snow Labs +name: clinical_deidentification +date: 2023-06-13 +tags: [deid, deidentification, pt, licensed] +task: [De-identification, Pipeline Healthcare] +language: pt +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pipeline is trained with `w2v_cc_300d` portuguese embeddings and can be used to deidentify PHI information from medical texts in Spanish. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask, fake or obfuscate the following entities: `AGE`, `DATE`, `PROFESSION`, `EMAIL`, `ID`, `COUNTRY`, `STREET`, `DOCTOR`, `HOSPITAL`, `PATIENT`, `URL`, `IP`, `ORGANIZATION`, `PHONE`, `ZIP`, `ACCOUNT`, `SSN`, `PLATE`, `SEX` and `IPADDR` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_pt_4.4.4_3.4_1686676413405.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_pt_4.4.4_3.4_1686676413405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "pt", "clinical/models") + +sample = """Dados do paciente. +Nome: Mauro. +Apelido: Gonçalves. +NIF: 368503. +NISS: 26 63514095. +Endereço: Calle Miguel Benitez 90. +CÓDIGO POSTAL: 28016. +Dados de cuidados. +Data de nascimento: 03/03/1946. +País: Portugal. +Idade: 70 anos Sexo: M. +Data de admissão: 12/12/2016. +Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973. +Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia. +Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal. +O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV. +A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal. +Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml. +A citologia da urina era repetidamente desconfiada por malignidade. +A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis. +A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso. +O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal. +A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga. +Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com. +""" + +result = deid_pipeline .annotate(sample) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "pt", "clinical/models") + +sample = "Dados do paciente. +Nome: Mauro. +Apelido: Gonçalves. +NIF: 368503. +NISS: 26 63514095. +Endereço: Calle Miguel Benitez 90. +CÓDIGO POSTAL: 28016. +Dados de cuidados. +Data de nascimento: 03/03/1946. +País: Portugal. +Idade: 70 anos Sexo: M. +Data de admissão: 12/12/2016. +Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973. +Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia. +Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal. +O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV. +A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal. +Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml. +A citologia da urina era repetidamente desconfiada por malignidade. +A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis. +A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso. +O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal. +A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga. +Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com" + +val result = deid_pipeline.annotate(sample) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.deid.clinical").predict("""Dados do paciente. +Nome: Mauro. +Apelido: Gonçalves. +NIF: 368503. +NISS: 26 63514095. +Endereço: Calle Miguel Benitez 90. +CÓDIGO POSTAL: 28016. +Dados de cuidados. +Data de nascimento: 03/03/1946. +País: Portugal. +Idade: 70 anos Sexo: M. +Data de admissão: 12/12/2016. +Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973. +Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia. +Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal. +O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV. +A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal. +Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml. +A citologia da urina era repetidamente desconfiada por malignidade. +A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis. +A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso. +O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal. +A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga. +Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com. +""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "pt", "clinical/models") + +sample = """Dados do paciente. +Nome: Mauro. +Apelido: Gonçalves. +NIF: 368503. +NISS: 26 63514095. +Endereço: Calle Miguel Benitez 90. +CÓDIGO POSTAL: 28016. +Dados de cuidados. +Data de nascimento: 03/03/1946. +País: Portugal. +Idade: 70 anos Sexo: M. +Data de admissão: 12/12/2016. +Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973. +Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia. +Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal. +O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV. +A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal. +Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml. +A citologia da urina era repetidamente desconfiada por malignidade. +A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis. +A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso. +O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal. +A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga. +Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com. +""" + +result = deid_pipeline .annotate(sample) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "pt", "clinical/models") + +sample = "Dados do paciente. +Nome: Mauro. +Apelido: Gonçalves. +NIF: 368503. +NISS: 26 63514095. +Endereço: Calle Miguel Benitez 90. +CÓDIGO POSTAL: 28016. +Dados de cuidados. +Data de nascimento: 03/03/1946. +País: Portugal. +Idade: 70 anos Sexo: M. +Data de admissão: 12/12/2016. +Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973. +Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia. +Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal. +O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV. +A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal. +Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml. +A citologia da urina era repetidamente desconfiada por malignidade. +A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis. +A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso. +O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal. +A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga. +Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com" + +val result = deid_pipeline.annotate(sample) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("pt.deid.clinical").predict("""Dados do paciente. +Nome: Mauro. +Apelido: Gonçalves. +NIF: 368503. +NISS: 26 63514095. +Endereço: Calle Miguel Benitez 90. +CÓDIGO POSTAL: 28016. +Dados de cuidados. +Data de nascimento: 03/03/1946. +País: Portugal. +Idade: 70 anos Sexo: M. +Data de admissão: 12/12/2016. +Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973. +Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia. +Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal. +O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV. +A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal. +Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml. +A citologia da urina era repetidamente desconfiada por malignidade. +A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis. +A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso. +O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal. +A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga. +Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com. +""") +``` +
+ +## Results + +```bash +Results + + +Masked with entity labels +------------------------------ +Dados do . +Nome: . +Apelido: . +NIF: . +NISS: . +Endereço: . +CÓDIGO POSTAL: . +Dados de cuidados. +Data de nascimento: . +País: . +Idade: anos Sexo: . +Data de admissão: . +Doutor: Cuéllar NºCol: . +Relatório clínico do : de anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; +Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia. +Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal. +O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV. +A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal. +Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicér de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml. +A citologia da urina era repetidamente desconfiada por malignidade. +A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis. +A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso. +O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. +A tomografia computorizada abdominal é normal. +A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga. +Referido por: - , 22 E-mail: . + +Masked with chars +------------------------------ +Dados do [******]. +Nome: [***]. +Apelido: [*******]. +NIF: [****]. +NISS: [*********]. +Endereço: [*********************]. +CÓDIGO POSTAL: [***]. +Dados de cuidados. +Data de nascimento: [********]. +País: [******]. +Idade: ** anos Sexo: *. +Data de admissão: [********]. +Doutor: [*************] Cuéllar NºCol: ** ** [***]. +Relatório clínico do [******]: [******] de ** anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; +Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia. +Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal. +O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV. +A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal. +Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicér[**] de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml. +A citologia da urina era repetidamente desconfiada por malignidade. +A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis. +A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso. +O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. +A tomografia computorizada abdominal é normal. +A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga. +Referido por: [***********] - [*****************], 22 [******] E-mail: [****************]. + +Masked with fixed length chars +------------------------------ +Dados do ****. +Nome: ****. +Apelido: ****. +NIF: ****. +NISS: ****. +Endereço: ****. +CÓDIGO POSTAL: ****. +Dados de cuidados. +Data de nascimento: ****. +País: ****. +Idade: **** anos Sexo: ****. +Data de admissão: ****. +Doutor: **** Cuéllar NºCol: **** **** ****. +Relatório clínico do ****: **** de **** anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; +Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia. +Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal. +O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV. +A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal. +Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicér**** de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml. +A citologia da urina era repetidamente desconfiada por malignidade. +A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis. +A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso. +O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. +A tomografia computorizada abdominal é normal. +A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga. +Referido por: **** - ****, 22 **** E-mail: ****. + +Obfuscated +------------------------------ +Dados do H.. +Nome: Marcos Alves. +Apelido: Tiago Santos. +NIF: 566-445. +NISS: 134544332. +Endereço: Rua de Santa María, 100. +CÓDIGO POSTAL: 4099. +Dados de cuidados. +Data de nascimento: 31/03/1946. +País: Espanha. +Idade: 46 anos Sexo: Mulher. +Data de admissão: 06/01/2017. +Doutor: Carlos Melo Cuéllar NºCol: 134544332 134544332 124 445 311. +Relatório clínico do H.: M. de 46 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; +Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia. +Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal. +O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV. +A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal. +Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicérHomen de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml. +A citologia da urina era repetidamente desconfiada por malignidade. +A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis. +A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso. +O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. +A tomografia computorizada abdominal é normal. +A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga. +Referido por: Carlos Melo - Avenida Dos Aliados, 56, 22 Espanha E-mail: maria.prado@jacob.com. + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_deidentification| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|pt| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- TextMatcherModel +- ContextualParserModel +- ContextualParserModel +- RegexMatcherModel +- RegexMatcherModel +- ChunkMergeModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_ro.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_ro.md new file mode 100644 index 0000000000..1f4fcd5d96 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_ro.md @@ -0,0 +1,191 @@ +--- +layout: model +title: Clinical Deidentification Pipeline (Romanian) +author: John Snow Labs +name: clinical_deidentification +date: 2023-06-13 +tags: [licensed, clinical, ro, deid, deidentification] +task: Pipeline Healthcare +language: ro +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pipeline is trained with `w2v_cc_300d` Romanian embeddings and can be used to deidentify PHI information from medical texts in Romanian. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask, fake or obfuscate the following entities: `AGE`, `CITY`, `COUNTRY`, `DATE`, `DOCTOR`, `EMAIL`, `FAX`, `HOSPITAL`, `IDNUM`, `LOCATION-OTHER`, `MEDICALRECORD`, `ORGANIZATION`, `PATIENT`, `PHONE`, `PROFESSION`, `STREET`, `ZIP`, `ACCOUNT`, `LICENSE`, `PLATE` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_ro_4.4.4_3.4_1686676662489.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_ro_4.4.4_3.4_1686676662489.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "ro", "clinical/models") + +sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 +Varsta : 77, Nume si Prenume : BUREAN MARIA +Tel: +40(235)413773, E-mail : hale@gmail.com, +Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, +Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """ + +result = deid_pipeline.annotate(sample) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "ro", "clinical/models") + +val sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 +Varsta : 77, Nume si Prenume : BUREAN MARIA +Tel: +40(235)413773, E-mail : hale@gmail.com, +Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, +Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """ + +val result = deid_pipeline.annotate(sample) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ro.deid.clinical").predict("""Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 +Varsta : 77, Nume si Prenume : BUREAN MARIA +Tel: +40(235)413773, E-mail : hale@gmail.com, +Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, +Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """) +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification", "ro", "clinical/models") + +sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 +Varsta : 77, Nume si Prenume : BUREAN MARIA +Tel: +40(235)413773, E-mail : hale@gmail.com, +Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, +Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """ + +result = deid_pipeline.annotate(sample) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "ro", "clinical/models") + +val sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 +Varsta : 77, Nume si Prenume : BUREAN MARIA +Tel: +40(235)413773, E-mail : hale@gmail.com, +Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, +Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """ + +val result = deid_pipeline.annotate(sample) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ro.deid.clinical").predict("""Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 +Varsta : 77, Nume si Prenume : BUREAN MARIA +Tel: +40(235)413773, E-mail : hale@gmail.com, +Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, +Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """) +``` +
+ +## Results + +```bash +Results + + +Masked with entity labels +------------------------------ +Medic : Dr. , C.N.P : , Data setului de analize: +Varsta : , Nume si Prenume : +Tel: , E-mail : , +Licență : , Înmatriculare : , Cont : , + , + +Masked with chars +------------------------------ +Medic : Dr. [**********], C.N.P : [***********], Data setului de analize: [*********] +Varsta : **, Nume si Prenume : [**********] +Tel: [************], E-mail : [************], +Licență : [*********], Înmatriculare : [******], Cont : [******************], +[**************************] [******************] [****], [****] + +Masked with fixed length chars +------------------------------ +Medic : Dr. ****, C.N.P : ****, Data setului de analize: **** +Varsta : ****, Nume si Prenume : **** +Tel: ****, E-mail : ****, +Licență : ****, Înmatriculare : ****, Cont : ****, +**** **** ****, **** + +Obfuscated +------------------------------ +Medic : Dr. Doina Gheorghiu, C.N.P : 6794561192919, Data setului de analize: 01-04-2001 +Varsta : 91, Nume si Prenume : Dragomir Emilia +Tel: 0248 551 376, E-mail : tudorsmaranda@kappa.ro, +Licență : T003485962M, Înmatriculare : AR-65-UPQ, Cont : KHHO5029180812813651, +Centrul Medical de Evaluare si Recuperare pentru Copii si Tineri Cristian Serban Buzias Aleea Voinea Curcani, 328479 + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_deidentification| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|ro| +|Size:|1.2 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ChunkMergeModel +- ChunkMergeModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_slim_en.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_slim_en.md new file mode 100644 index 0000000000..514e5f2506 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_slim_en.md @@ -0,0 +1,212 @@ +--- +layout: model +title: Clinical Deidentification Pipeline (English, slim) +author: John Snow Labs +name: clinical_deidentification_slim +date: 2023-06-13 +tags: [deidentification, deid, glove, slim, pipeline, clinical, en, licensed] +task: Pipeline Healthcare +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pipeline is trained with lightweight `glove_100d` embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate `AGE`, `CONTACT`, `DATE`, `ID`, `LOCATION`, `NAME`, `PROFESSION`, `CITY`, `COUNTRY`, `DOCTOR`, `HOSPITAL`, `IDNUM`, `MEDICALRECORD`, `ORGANIZATION`, `PATIENT`, `PHONE`, `PROFESSION`, `STREET`, `USERNAME`, `ZIP`, `ACCOUNT`, `LICENSE`, `VIN`, `SSN`, `DLN`, `PLATE`, `IPADDR`, `EMAIL` entities. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_slim_en_4.4.4_3.4_1686676716215.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_slim_en_4.4.4_3.4_1686676716215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification_slim", "en", "clinical/models") + +sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. +Dr. John Green, ID: 1231511863, IP 203.120.223.13. +He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. +Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. +Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""" + +result = deid_pipeline.annotate(sample) +print("\n".join(result['masked'])) +print("\n".join(result['masked_with_chars'])) +print("\n".join(result['masked_fixed_length_chars'])) +print("\n".join(result['obfuscated'])) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val deid_pipeline = new PretrainedPipeline("clinical_deidentification_slim","en","clinical/models") + +val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. +Dr. John Green, ID: 1231511863, IP 203.120.223.13. +He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. +Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. +Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""" + +val result = deid_pipeline.annotate(sample) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.de_identify.clinical_slim").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. +Dr. John Green, ID: 1231511863, IP 203.120.223.13. +He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. +Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. +Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +deid_pipeline = PretrainedPipeline("clinical_deidentification_slim", "en", "clinical/models") + +sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. +Dr. John Green, ID: 1231511863, IP 203.120.223.13. +He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. +Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. +Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""" + +result = deid_pipeline.annotate(sample) +print("\n".join(result['masked'])) +print("\n".join(result['masked_with_chars'])) +print("\n".join(result['masked_fixed_length_chars'])) +print("\n".join(result['obfuscated'])) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val deid_pipeline = new PretrainedPipeline("clinical_deidentification_slim","en","clinical/models") + +val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. +Dr. John Green, ID: 1231511863, IP 203.120.223.13. +He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. +Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. +Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""" + +val result = deid_pipeline.annotate(sample) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.de_identify.clinical_slim").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. +Dr. John Green, ID: 1231511863, IP 203.120.223.13. +He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. +Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. +Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""") +``` +
+ +## Results + +```bash +Results + + +Masked with entity labels +------------------------------ +Name : , Record date: , # . +Dr. , ID: , IP . +He is a male was admitted to the for cystectomy on . +Patient's VIN : , SSN , Driver's license . +Phone , , , E-MAIL: . + +Masked with chars +------------------------------ +Name : [**************], Record date: [********], # [****]. +Dr. [********], ID: [********], IP [************]. +He is a [*********] male was admitted to the [**********] for cystectomy on [******]. +Patient's VIN : [***************], SSN [**********], Driver's license [*********]. +Phone [************], [***************], [***********], E-MAIL: [*************]. + +Masked with fixed length chars +------------------------------ +Name : ****, Record date: ****, # ****. +Dr. ****, ID: ****, IP ****. +He is a **** male was admitted to the **** for cystectomy on ****. +Patient's VIN : ****, SSN ****, Driver's license ****. +Phone ****, ****, ****, E-MAIL: ****. + +Obfuscated +------------------------------ +Name : Layne Nation, Record date: 2093-03-13, # C6240488. +Dr. Dr Rosalba Hill, ID: JY:3489547, IP 005.005.005.005. +He is a 79 male was admitted to the JOHN MUIR MEDICAL CENTER-CONCORD CAMPUS for cystectomy on 01-25-1997. +Patient's VIN : 3CCCC22DDDD333888, SSN SSN-289-37-4495, Driver's license S99983662. +Phone 04.32.52.27.90, North Adrienne, Colorado Springs, E-MAIL: Rawland@google.com. + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_deidentification_slim| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|181.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- MedicalNerModel +- NerConverter +- ChunkMergeModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- TextMatcherModel +- ContextualParserModel +- RegexMatcherModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ContextualParserModel +- ChunkMergeModel +- ChunkMergeModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- DeIdentificationModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-icd10_icd9_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-icd10_icd9_mapping_en.md new file mode 100644 index 0000000000..3cc7f708bd --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-icd10_icd9_mapping_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: Pipeline to Mapping ICD10-CM Codes with Their Corresponding ICD-9-CM Codes +author: John Snow Labs +name: icd10_icd9_mapping +date: 2023-06-13 +tags: [en, licensed, icd10cm, icd9, pipeline, chunk_mapping] +task: Chunk Mapping +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of `icd10_icd9_mapper` model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/icd10_icd9_mapping_en_4.4.4_3.4_1686674479654.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/icd10_icd9_mapping_en_4.4.4_3.4_1686674479654.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(Z833 A0100 A000) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(Z833 A0100 A000) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(Z833 A0100 A000) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(Z833 A0100 A000) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""") +``` +
+ +## Results + +```bash +Results + + + +| | icd10_code | icd9_code | +|---:|:--------------------|:-------------------| +| 0 | Z833 | A0100 | A000 | V180 | 0020 | 0010 | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|icd10_icd9_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|593.6 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ChunkMapperModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-icd10cm_snomed_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-icd10cm_snomed_mapping_en.md new file mode 100644 index 0000000000..982774103a --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-icd10cm_snomed_mapping_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: Pipeline to Mapping ICD10-CM Codes with Their Corresponding SNOMED Codes +author: John Snow Labs +name: icd10cm_snomed_mapping +date: 2023-06-13 +tags: [en, licensed, icd10cm, snomed, pipeline, chunk_mapping] +task: Chunk Mapping +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of `icd10cm_snomed_mapper` model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/icd10cm_snomed_mapping_en_4.4.4_3.4_1686676495857.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/icd10cm_snomed_mapping_en_4.4.4_3.4_1686676495857.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(R079 N4289 M62830) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(R079 N4289 M62830) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(R079 N4289 M62830) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(R079 N4289 M62830) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here.""") +``` +
+ +## Results + +```bash +Results + + + +| | icd10cm_code | snomed_code | +|---:|:----------------------|:-----------------------------------------| +| 0 | R079 | N4289 | M62830 | 161972006 | 22035000 | 16410651000119105 | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|icd10cm_snomed_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.1 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ChunkMapperModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-icd10cm_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-icd10cm_umls_mapping_en.md new file mode 100644 index 0000000000..8852980f46 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-icd10cm_umls_mapping_en.md @@ -0,0 +1,117 @@ +--- +layout: model +title: ICD10 to UMLS Code Mapping +author: John Snow Labs +name: icd10cm_umls_mapping +date: 2023-06-13 +tags: [en, licensed, icd10cm, umls, pipeline, chunk_mapping] +task: Chunk Mapping +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline maps ICD10CM codes to UMLS codes without using any text data. You’ll just feed white space-delimited ICD10CM codes and it will return the corresponding UMLS codes as a list. If there is no mapping, the original code is returned with no mapping. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/icd10cm_umls_mapping_en_4.4.4_3.4_1686674445551.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/icd10cm_umls_mapping_en_4.4.4_3.4_1686674445551.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901']) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901']) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901']) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901']) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""") +``` +
+ +## Results + +```bash +Results + + + +{'icd10cm': ['M89.50', 'R82.2', 'R09.01'], +'umls': ['C4721411', 'C0159076', 'C0004044']} + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|icd10cm_umls_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|956.6 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ChunkMapperModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-icdo_snomed_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-icdo_snomed_mapping_en.md new file mode 100644 index 0000000000..9f9a2d8eef --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-icdo_snomed_mapping_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: Pipeline to Mapping ICDO Codes with Their Corresponding SNOMED Codes +author: John Snow Labs +name: icdo_snomed_mapping +date: 2023-06-13 +tags: [en, licensed, clinical, resolver, pipeline, chunk_mapping, icdo, snomed] +task: Chunk Mapping +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of `icdo_snomed_mapper` model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/icdo_snomed_mapping_en_4.4.4_3.4_1686676498396.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/icdo_snomed_mapping_en_4.4.4_3.4_1686676498396.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(8120/1 8170/3 8380/3) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(8120/1 8170/3 8380/3) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""") +``` +
+ +## Results + +```bash +Results + + + +| | icdo_code | snomed_code | +|---:|:-------------------------|:-------------------------------| +| 0 | 8120/1 | 8170/3 | 8380/3 | 45083001 | 25370001 | 30289006 | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|icdo_snomed_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|137.3 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ChunkMapperModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-mesh_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-mesh_umls_mapping_en.md new file mode 100644 index 0000000000..7f1d58098b --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-mesh_umls_mapping_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: Pipeline to Mapping MESH Codes with Their Corresponding UMLS Codes +author: John Snow Labs +name: mesh_umls_mapping +date: 2023-06-13 +tags: [en, licensed, clinical, resolver, pipeline, chunk_mapping, mesh, umls] +task: Chunk Mapping +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of `mesh_umls_mapper` model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/mesh_umls_mapping_en_4.4.4_3.4_1686674448528.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/mesh_umls_mapping_en_4.4.4_3.4_1686674448528.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(C028491 D019326 C579867) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(C028491 D019326 C579867) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(C028491 D019326 C579867) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(C028491 D019326 C579867) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""") +``` +
+ +## Results + +```bash +Results + + + +| | mesh_code | umls_code | +|---:|:----------------------------|:-------------------------------| +| 0 | C028491 | D019326 | C579867 | C0043904 | C0045010 | C3696376 | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mesh_umls_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|3.9 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ChunkMapperModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-ner_deid_generic_augmented_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-ner_deid_generic_augmented_pipeline_en.md new file mode 100644 index 0000000000..10ce8f7973 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-ner_deid_generic_augmented_pipeline_en.md @@ -0,0 +1,127 @@ +--- +layout: model +title: Pipeline to Detect PHI for Deidentification (Generic - Augmented) +author: John Snow Labs +name: ner_deid_generic_augmented_pipeline +date: 2023-06-13 +tags: [licensed, ner, clinical, deidentification, generic, en] +task: Named Entity Recognition +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [ner_deid_generic_augmented](https://nlp.johnsnowlabs.com/2021/06/30/ner_deid_generic_augmented_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_generic_augmented_pipeline_en_4.4.4_3.4_1686674972743.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ner_deid_generic_augmented_pipeline_en_4.4.4_3.4_1686674972743.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models") + +pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.") +``` +```scala +val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models") + +pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models") + +pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.") +``` +```scala +val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models") + +pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""") +``` +
+ +## Results + +```bash +Results + + ++-------------------------------------------------+---------+ +|chunk |ner_label| ++-------------------------------------------------+---------+ +|2093-01-13 |DATE | +|David Hale |NAME | +|Hendrickson |NAME | +|Ora MR. |LOCATION | +|7194334 |ID | +|01/13/93 |DATE | +|Oliveira |NAME | +|25 |AGE | +|1-11-2000 |DATE | +|Cocke County Baptist Hospital. 0295 Keats Street.|LOCATION | +|(302) 786-5227 |CONTACT | ++-------------------------------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ner_deid_generic_augmented_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-ner_deid_subentity_pipeline_ar.md b/docs/_posts/Cabir40/2023-06-13-ner_deid_subentity_pipeline_ar.md new file mode 100644 index 0000000000..323025361c --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-ner_deid_subentity_pipeline_ar.md @@ -0,0 +1,133 @@ +--- +layout: model +title: Pipeline for Detect Subentity PHI for Deidentification (Arabic) +author: John Snow Labs +name: ner_deid_subentity_pipeline +date: 2023-06-13 +tags: [licensed, clinical, deidentification, ar, pipeline] +task: Pipeline Healthcare +language: ar +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp.johnsnowlabs.com/2023/05/29/ner_deid_subentity_ar.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_subentity_pipeline_ar_4.4.4_3.4_1686677138545.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ner_deid_subentity_pipeline_ar_4.4.4_3.4_1686677138545.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models") + +text= ''' +ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح. +''' + +result = pipeline.fullAnnotate(text) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models") + + +val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح." + +val result = pipeline.fullAnnotate(text) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models") + +text= ''' +ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح. +''' + +result = pipeline.fullAnnotate(text) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models") + + +val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح." + +val result = pipeline.fullAnnotate(text) +``` +
+ +## Results + +```bash +Results + + + ++---------------+--------+ +|chunks |entities| ++---------------+--------+ +|16 أبريل 2000 |DATE | +|ليلى حسن |PATIENT | +|789، |ZIP | +|جدة |CITY | +|54321 |ZIP | +|المملكة العربية|CITY | +|السعودية |COUNTRY | +|النور |HOSPITAL| +|أميرة أحمد |DOCTOR | +|ليلى |PATIENT | +|35 |AGE | ++---------------+--------+ + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ner_deid_subentity_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|ar| +|Size:|1.2 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-ner_medication_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-ner_medication_pipeline_en.md new file mode 100644 index 0000000000..3c8cf4496d --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-ner_medication_pipeline_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Pipeline for Detect Medication +author: John Snow Labs +name: ner_medication_pipeline +date: 2023-06-13 +tags: [ner, en, licensed] +task: Pipeline Healthcare +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +A pretrained pipeline to detect medication entities. It was built on the top of `ner_posology_greedy` model and also augmented with the drug names mentioned in UK and US drugbank datasets. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_medication_pipeline_en_4.4.4_3.4_1686676807396.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ner_medication_pipeline_en_4.4.4_3.4_1686676807396.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models") + +text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg.""" + +result = ner_medication_pipeline.fullAnnotate([text]) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models") + +val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."") +``` + +{:.nlu-block} +```python +| ner_chunk | entity | +|:-------------------|:---------| +| metformin 1000 MG | DRUG | +| glipizide 2.5 MG | DRUG | +| Fragmin 5000 units | DRUG | +| Xenaderm | DRUG | +| OxyContin 30 mg | DRUG | +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models") + +text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg.""" + +result = ner_medication_pipeline.fullAnnotate([text]) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models") + +val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."") +``` + +{:.nlu-block} +```python +| ner_chunk | entity | +|:-------------------|:---------| +| metformin 1000 MG | DRUG | +| glipizide 2.5 MG | DRUG | +| Fragmin 5000 units | DRUG | +| Xenaderm | DRUG | +| OxyContin 30 mg | DRUG | +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ner_medication_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverterInternalModel +- TextMatcherModel +- ChunkMergeModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-re_bodypart_directions_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_bodypart_directions_pipeline_en.md new file mode 100644 index 0000000000..46198d8d2d --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-re_bodypart_directions_pipeline_en.md @@ -0,0 +1,133 @@ +--- +layout: model +title: RE Pipeline between Body Parts and Direction Entities +author: John Snow Labs +name: re_bodypart_directions_pipeline +date: 2023-06-13 +tags: [licensed, clinical, relation_extraction, body_part, directions, en] +task: Relation Extraction +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [re_bodypart_directions](https://nlp.johnsnowlabs.com/2021/01/18/re_bodypart_directions_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/re_bodypart_directions_pipeline_en_4.4.4_3.4_1686675334454.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/re_bodypart_directions_pipeline_en_4.4.4_3.4_1686675334454.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia""") +``` +
+ +## Results + +```bash +Results + + + +| index | relations | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2 | confidence | +|-------|-----------|-----------------------------|---------------|-------------|------------|-----------------------------|-------------|-------------|---------------|------------| +| 0 | 1 | Direction | 35 | 39 | upper | Internal_organ_or_component | 41 | 50 | brain stem | 0.9999989 | +| 1 | 0 | Direction | 35 | 39 | upper | Internal_organ_or_component | 59 | 68 | cerebellum | 0.99992585 | +| 2 | 0 | Direction | 35 | 39 | upper | Internal_organ_or_component | 81 | 93 | basil ganglia | 0.9999999 | +| 3 | 0 | Internal_organ_or_component | 41 | 50 | brain stem | Direction | 54 | 57 | left | 0.999811 | +| 4 | 0 | Internal_organ_or_component | 41 | 50 | brain stem | Direction | 75 | 79 | right | 0.9998203 | +| 5 | 1 | Direction | 54 | 57 | left | Internal_organ_or_component | 59 | 68 | cerebellum | 1.0 | +| 6 | 0 | Direction | 54 | 57 | left | Internal_organ_or_component | 81 | 93 | basil ganglia | 0.97616416 | +| 7 | 0 | Internal_organ_or_component | 59 | 68 | cerebellum | Direction | 75 | 79 | right | 0.953046 | +| 8 | 1 | Direction | 75 | 79 | right | Internal_organ_or_component | 81 | 93 | basil ganglia | 1.0 | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|re_bodypart_directions_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- DependencyParserModel +- RelationExtractionModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-re_bodypart_proceduretest_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_bodypart_proceduretest_pipeline_en.md new file mode 100644 index 0000000000..0c5d316dea --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-re_bodypart_proceduretest_pipeline_en.md @@ -0,0 +1,125 @@ +--- +layout: model +title: RE Pipeline between Body Parts and Procedures +author: John Snow Labs +name: re_bodypart_proceduretest_pipeline +date: 2023-06-13 +tags: [licensed, clinical, relation_extraction, body_part, procedures, en] +task: Relation Extraction +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [re_bodypart_proceduretest](https://nlp.johnsnowlabs.com/2021/01/18/re_bodypart_proceduretest_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/re_bodypart_proceduretest_pipeline_en_4.4.4_3.4_1686675479605.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/re_bodypart_proceduretest_pipeline_en_4.4.4_3.4_1686675479605.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""") +``` +
+ +## Results + +```bash +Results + + + +| index | relations | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2 | confidence | +|-------|-----------|------------------------------|---------------|-------------|--------|---------|-------------|-------------|---------------------|------------| +| 0 | 1 | External_body_part_or_region | 94 | 98 | chest | Test | 117 | 135 | portable ultrasound | 1.0 | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|re_bodypart_proceduretest_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- DependencyParserModel +- RelationExtractionModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md new file mode 100644 index 0000000000..62a0635224 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Pipeline to Detect Relations Between Genes and Phenotypes +author: John Snow Labs +name: re_human_phenotype_gene_clinical_pipeline +date: 2023-06-13 +tags: [licensed, clinical, re, genes, phenotypes, en] +task: Relation Extraction +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [re_human_phenotype_gene_clinical](https://nlp.johnsnowlabs.com/2020/09/30/re_human_phenotype_gene_clinical_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/re_human_phenotype_gene_clinical_pipeline_en_4.4.4_3.4_1686675625159.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/re_human_phenotype_gene_clinical_pipeline_en_4.4.4_3.4_1686675625159.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3") +``` +```scala +val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3") +``` +```scala +val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""") +``` +
+ +## Results + +```bash +Results + + ++----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+ +| | relation | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_begin | entity2_end | chunk2 | confidence | ++====+============+===========+=================+===============+=====================+===========+=================+===============+=====================+==============+ +| 0 | 1 | HP | 23 | 36 | microphthalmia | HP | 42 | 60 | developmental delay | 0.999954 | ++----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+ +| 1 | 1 | HP | 23 | 36 | microphthalmia | GENE | 110 | 114 | TENM3 | 0.999999 | ++----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|re_human_phenotype_gene_clinical_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- DependencyParserModel +- RelationExtractionModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-re_temporal_events_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_temporal_events_clinical_pipeline_en.md new file mode 100644 index 0000000000..1a2a14ca40 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-re_temporal_events_clinical_pipeline_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Pipeline to Detect Temporal Relations for Clinical Events +author: John Snow Labs +name: re_temporal_events_clinical_pipeline +date: 2023-06-13 +tags: [licensed, clinical, relation_extraction, events, en] +task: Relation Extraction +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [re_temporal_events_clinical](https://nlp.johnsnowlabs.com/2020/09/28/re_temporal_events_clinical_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/re_temporal_events_clinical_pipeline_en_4.4.4_3.4_1686675769364.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/re_temporal_events_clinical_pipeline_en_4.4.4_3.4_1686675769364.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.") +``` +```scala +val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.") +``` +```scala +val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""") +``` +
+ +## Results + +```bash +Results + + ++----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+ +| | relation | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_begin | entity2_end | chunk2 | confidence | ++====+============+============+=================+===============+==========================+===========+=================+===============+=====================+==============+ +| 0 | OVERLAP | OCCURRENCE | 121 | 144 | a motor vehicle accident | DATE | 149 | 165 | September of 2005 | 0.999975 | ++----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+ +| 1 | OVERLAP | DATE | 171 | 179 | that time | PROBLEM | 201 | 219 | any specific injury | 0.956654 | ++----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|re_temporal_events_clinical_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- DependencyParserModel +- RelationExtractionModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md new file mode 100644 index 0000000000..71e8df6e4d --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Pipeline to Detect Temporal Relations for Clinical Events (Enriched) +author: John Snow Labs +name: re_temporal_events_enriched_clinical_pipeline +date: 2023-06-13 +tags: [licensed, clinical, relation_extraction, event, enriched, en] +task: Relation Extraction +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [re_temporal_events_enriched_clinical](https://nlp.johnsnowlabs.com/2020/09/28/re_temporal_events_enriched_clinical_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/re_temporal_events_enriched_clinical_pipeline_en_4.4.4_3.4_1686675915099.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/re_temporal_events_enriched_clinical_pipeline_en_4.4.4_3.4_1686675915099.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.") +``` +```scala +val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.") +``` +```scala +val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models") + + +pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""") +``` +
+ +## Results + +```bash +Results + + ++----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+ +| | relation | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_begin | entity2_end | chunk2 | confidence | ++====+============+===========+=================+===============+===============================================+============+=================+===============+==========================+==============+ +| 0 | OVERLAP | PROBLEM | 54 | 98 | longstanding intermittent right low back pain | OCCURRENCE | 121 | 144 | a motor vehicle accident | 0.532308 | ++----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+ +| 1 | AFTER | DATE | 171 | 179 | that time | PROBLEM | 201 | 219 | any specific injury | 0.577288 | ++----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|re_temporal_events_enriched_clinical_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- DependencyParserModel +- RelationExtractionModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-re_test_problem_finding_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_test_problem_finding_pipeline_en.md new file mode 100644 index 0000000000..455b44b63d --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-re_test_problem_finding_pipeline_en.md @@ -0,0 +1,125 @@ +--- +layout: model +title: RE Pipeline between Problem, Test, and Findings in Reports +author: John Snow Labs +name: re_test_problem_finding_pipeline +date: 2023-06-13 +tags: [licensed, clinical, relation_extraction, problem, test, findings, en] +task: Relation Extraction +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [re_test_problem_finding](https://nlp.johnsnowlabs.com/2021/04/19/re_test_problem_finding_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/re_test_problem_finding_pipeline_en_4.4.4_3.4_1686676068152.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/re_test_problem_finding_pipeline_en_4.4.4_3.4_1686676068152.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""") +``` +
+ +## Results + +```bash +Results + + + +| index | relations | entity1 | chunk1 | entity2 | chunk2 | +|-------|--------------|--------------|---------------------|--------------|---------| +| 0 | 1 | PROCEDURE | biopsy | SYMPTOM | lesion | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|re_test_problem_finding_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- DependencyParserModel +- RelationExtractionModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-re_test_result_date_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_test_result_date_pipeline_en.md new file mode 100644 index 0000000000..8a0c3c145f --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-re_test_result_date_pipeline_en.md @@ -0,0 +1,127 @@ +--- +layout: model +title: RE Pipeline between Tests, Results, and Dates +author: John Snow Labs +name: re_test_result_date_pipeline +date: 2023-06-13 +tags: [licensed, clinical, relation_extraction, tests, results, dates, en] +task: Relation Extraction +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [re_test_result_date](https://nlp.johnsnowlabs.com/2021/02/24/re_test_result_date_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/re_test_result_date_pipeline_en_4.4.4_3.4_1686676215339.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/re_test_result_date_pipeline_en_4.4.4_3.4_1686676215339.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models") + +pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""") +``` +
+ +## Results + +```bash +Results + + + +| index | relations | entity1 | chunk1 | entity2 | chunk2 | +|-------|--------------|--------------|---------------------|--------------|---------| +| 0 | O | TEST | chest X-ray | MEASUREMENTS | 93% | +| 1 | O | TEST | CT scan | MEASUREMENTS | 93% | +| 2 | is_result_of | TEST | SpO2 | MEASUREMENTS | 93% | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|re_test_result_date_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter +- DependencyParserModel +- RelationExtractionModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-recognize_entities_posology_en.md b/docs/_posts/Cabir40/2023-06-13-recognize_entities_posology_en.md new file mode 100644 index 0000000000..bcb96298e4 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-recognize_entities_posology_en.md @@ -0,0 +1,133 @@ +--- +layout: model +title: Pipeline for detecting posology entities +author: John Snow Labs +name: recognize_entities_posology +date: 2023-06-13 +tags: [pipeline, en, licensed, clinical] +task: Pipeline Healthcare +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +A pipeline with `ner_posology`. It will only extract medication entities. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/recognize_entities_posology_en_4.4.4_3.4_1686674383261.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/recognize_entities_posology_en_4.4.4_3.4_1686674383261.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models') + +res = pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . +She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals. +""") +``` +```scala +val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "clinical/models") + +val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . +She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals. +""")(0) + +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . +She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals. +""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models') + +res = pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . +She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals. +""") +``` +```scala +val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "clinical/models") + +val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . +She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals. +""")(0) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . +She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals. +""") +``` +
+ +## Results + +```bash +Results + + +| | chunk | begin | end | entity | +|---:|:-----------------|--------:|------:|:----------| +| 0 | metformin | 83 | 91 | DRUG | +| 1 | 1000 mg | 93 | 99 | STRENGTH | +| 2 | two times a day | 101 | 115 | FREQUENCY | +| 3 | 40 units | 270 | 277 | DOSAGE | +| 4 | insulin glargine | 282 | 297 | DRUG | +| 5 | at night | 299 | 306 | FREQUENCY | +| 6 | 12 units | 309 | 316 | DOSAGE | +| 7 | insulin lispro | 321 | 334 | DRUG | +| 8 | with meals | 336 | 345 | FREQUENCY | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_posology| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- MedicalNerModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-rxnorm_mesh_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-rxnorm_mesh_mapping_en.md new file mode 100644 index 0000000000..336c56f022 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-rxnorm_mesh_mapping_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: RxNorm to MeSH Code Mapping +author: John Snow Labs +name: rxnorm_mesh_mapping +date: 2023-06-13 +tags: [rxnorm, mesh, en, licensed, pipeline] +task: Pipeline Healthcare +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline maps RxNorm codes to MeSH codes without using any text data. You’ll just feed white space-delimited RxNorm codes and it will return the corresponding MeSH codes as a list. If there is no mapping, the original code is returned with no mapping. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/rxnorm_mesh_mapping_en_4.4.4_3.4_1686674451239.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/rxnorm_mesh_mapping_en_4.4.4_3.4_1686674451239.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models") +pipeline.annotate("1191 6809 47613") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline +val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models") +val result = pipeline.annotate("1191 6809 47613") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models") +pipeline.annotate("1191 6809 47613") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline +val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models") +val result = pipeline.annotate("1191 6809 47613") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""") +``` +
+ +## Results + +```bash +Results + + +{'rxnorm': ['1191', '6809', '47613'], +'mesh': ['D001241', 'D008687', 'D019355']} + + +Note: + +| RxNorm | Details | +| ---------- | -------------------:| +| 1191 | aspirin | +| 6809 | metformin | +| 47613 | calcium citrate | + +| MeSH | Details | +| ---------- | -------------------:| +| D001241 | Aspirin | +| D008687 | Metformin | +| D019355 | Calcium Citrate | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rxnorm_mesh_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|103.6 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- LemmatizerModel +- Finisher \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-rxnorm_ndc_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-rxnorm_ndc_mapping_en.md new file mode 100644 index 0000000000..3bd7cd4a80 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-rxnorm_ndc_mapping_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Pipeline to Mapping RxNorm Codes with Corresponding National Drug Codes (NDC) +author: John Snow Labs +name: rxnorm_ndc_mapping +date: 2023-06-13 +tags: [en, licensed, clinical, pipeline, chunk_mapping, rxnorm, ndc] +task: Chunk Mapping +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline maps RXNORM codes to NDC codes without using any text data. You’ll just feed white space-delimited RXNORM codes and it will return the corresponding two different types of ndc codes which are called `package ndc` and `product ndc`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/rxnorm_ndc_mapping_en_4.4.4_3.4_1686676501661.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/rxnorm_ndc_mapping_en_4.4.4_3.4_1686676501661.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(1652674 259934) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(1652674 259934) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(1652674 259934) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(1652674 259934) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""") +``` +
+ +## Results + +```bash +Results + + + +{'document': ['1652674 259934'], +'package_ndc': ['62135-0625-60', '13349-0010-39'], +'product_ndc': ['46708-0499', '13349-0010'], +'rxnorm_code': ['1652674', '259934']} + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rxnorm_ndc_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|4.0 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ChunkMapperModel +- ChunkMapperModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-rxnorm_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-rxnorm_umls_mapping_en.md new file mode 100644 index 0000000000..41e6ab3658 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-rxnorm_umls_mapping_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: Pipeline to Mapping RxNORM Codes with Their Corresponding UMLS Codes +author: John Snow Labs +name: rxnorm_umls_mapping +date: 2023-06-13 +tags: [en, licensed, clinical, pipeline, chunk_mapping, rxnorm, umls] +task: Chunk Mapping +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of `rxnorm_umls_mapper` model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/rxnorm_umls_mapping_en_4.4.4_3.4_1686674453772.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/rxnorm_umls_mapping_en_4.4.4_3.4_1686674453772.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(1161611 315677) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(1161611 315677) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(1161611 315677) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(1161611 315677) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""") +``` +
+ +## Results + +```bash +Results + + + +| | rxnorm_code | umls_code | +|---:|:-----------------|:--------------------| +| 0 | 1161611 | 315677 | C3215948 | C0984912 | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rxnorm_umls_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.9 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ChunkMapperModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-snomed_icd10cm_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-snomed_icd10cm_mapping_en.md new file mode 100644 index 0000000000..bb7b141366 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-snomed_icd10cm_mapping_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: Pipeline to Mapping SNOMED Codes with Their Corresponding ICD10-CM Codes +author: John Snow Labs +name: snomed_icd10cm_mapping +date: 2023-06-13 +tags: [en, licensed, clinical, pipeline, chunk_mapping, snomed, icd10cm] +task: Chunk Mapping +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of `snomed_icd10cm_mapper` model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/snomed_icd10cm_mapping_en_4.4.4_3.4_1686674442769.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/snomed_icd10cm_mapping_en_4.4.4_3.4_1686674442769.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(128041000119107 292278006 293072005) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(128041000119107 292278006 293072005) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(128041000119107 292278006 293072005) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(128041000119107 292278006 293072005) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""") +``` +
+ +## Results + +```bash +Results + + + +| | snomed_code | icd10cm_code | +|---:|:----------------------------------------|:---------------------------| +| 0 | 128041000119107 | 292278006 | 293072005 | K22.70 | T43.595 | T37.1X5 | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|snomed_icd10cm_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|1.5 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ChunkMapperModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-snomed_icdo_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-snomed_icdo_mapping_en.md new file mode 100644 index 0000000000..23e806b04a --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-snomed_icdo_mapping_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: Pipeline to Mapping SNOMED Codes with Their Corresponding ICDO Codes +author: John Snow Labs +name: snomed_icdo_mapping +date: 2023-06-13 +tags: [en, licensed, clinical, pipeline, chunk_mapping, snomed, icdo] +task: Chunk Mapping +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of `snomed_icdo_mapper` model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/snomed_icdo_mapping_en_4.4.4_3.4_1686676504551.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/snomed_icdo_mapping_en_4.4.4_3.4_1686676504551.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(10376009 2026006 26638004) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(10376009 2026006 26638004) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(10376009 2026006 26638004) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(10376009 2026006 26638004) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""") +``` +
+ +## Results + +```bash +Results + + + +| | snomed_code | icdo_code | +|---:|:------------------------------|:-------------------------| +| 0 | 10376009 | 2026006 | 26638004 | 8050/2 | 9014/0 | 8322/0 | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|snomed_icdo_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|212.8 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ChunkMapperModel \ No newline at end of file diff --git a/docs/_posts/Cabir40/2023-06-13-snomed_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-snomed_umls_mapping_en.md new file mode 100644 index 0000000000..bd24a041d5 --- /dev/null +++ b/docs/_posts/Cabir40/2023-06-13-snomed_umls_mapping_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: Pipeline to Mapping SNOMED Codes with Their Corresponding UMLS Codes +author: John Snow Labs +name: snomed_umls_mapping +date: 2023-06-13 +tags: [en, licensed, clinical, pipeline, chunk_mapping, snomed, umls] +task: Chunk Mapping +language: en +edition: Healthcare NLP 4.4.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of `snomed_umls_mapper` model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/snomed_umls_mapping_en_4.4.4_3.4_1686674457097.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/snomed_umls_mapping_en_4.4.4_3.4_1686674457097.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(733187009 449433008 51264003) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(733187009 449433008 51264003) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models") + +result = pipeline.fullAnnotate(733187009 449433008 51264003) +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models") + +val result = pipeline.fullAnnotate(733187009 449433008 51264003) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""") +``` +
+ +## Results + +```bash +Results + + + +| | snomed_code | umls_code | +|---:|:---------------------------------|:-------------------------------| +| 0 | 733187009 | 449433008 | 51264003 | C4546029 | C3164619 | C0271267 | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|snomed_umls_mapping| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.4+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|5.1 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ChunkMapperModel \ No newline at end of file diff --git a/docs/_posts/mauro-nievoff/2023-06-13-bert_sequence_classifier_vop_side_effect_pipeline_en.md b/docs/_posts/mauro-nievoff/2023-06-13-bert_sequence_classifier_vop_side_effect_pipeline_en.md new file mode 100644 index 0000000000..abed74f170 --- /dev/null +++ b/docs/_posts/mauro-nievoff/2023-06-13-bert_sequence_classifier_vop_side_effect_pipeline_en.md @@ -0,0 +1,76 @@ +--- +layout: model +title: Side Effect Classification Pipeline - Voice of the Patient +author: John Snow Labs +name: bert_sequence_classifier_vop_side_effect_pipeline +date: 2023-06-13 +tags: [pipeline, classification, side_effect, vop, clinical, en, licensed] +task: Text Classification +language: en +edition: Healthcare NLP 4.4.3 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline includes the Medical Bert for Sequence Classification model to classify health-related text in colloquial language according to the presence or absence of mentions of side effects. The pipeline is built on the top of [bert_sequence_classifier_vop_side_effect](https://nlp.johnsnowlabs.com/2023/05/24/bert_sequence_classifier_vop_side_effect_en.html) model. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_vop_side_effect_pipeline_en_4.4.3_3.2_1686700519111.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_vop_side_effect_pipeline_en_4.4.3_3.2_1686700519111.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +This pipeline includes the Medical Bert for Sequence Classification model to classify health-related text in colloquial language according to the presence or absence of mentions of side effects. + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("bert_sequence_classifier_vop_side_effect_pipeline", "en", "clinical/models") + +pipeline.annotate("I felt kind of dizzy after taking that medication for a month.") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("bert_sequence_classifier_vop_side_effect_pipeline", "en", "clinical/models") + +val result = pipeline.annotate(I felt kind of dizzy after taking that medication for a month.) +``` +
+ +## Results + +```bash +| text | prediction | +|:---------------------------------------------------------------|:-------------| +| I felt kind of dizzy after taking that medication for a month. | True | +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_vop_side_effect_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.3+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|406.4 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- MedicalBertForSequenceClassification diff --git a/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_drug_side_effect_pipeline_en.md b/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_drug_side_effect_pipeline_en.md new file mode 100644 index 0000000000..6820988c48 --- /dev/null +++ b/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_drug_side_effect_pipeline_en.md @@ -0,0 +1,77 @@ +--- +layout: model +title: Drug Side Effect Classification Pipeline - Voice of the Patient +author: John Snow Labs +name: bert_sequence_classifier_vop_drug_side_effect_pipeline +date: 2023-06-14 +tags: [clinical, licensed, en, classification, vop] +task: Text Classification +language: en +edition: Healthcare NLP 4.4.3 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline includes the Medical Bert for Sequence Classification model to classify health-related text in colloquial language according to the presence or absence of mentions of side effects related to drugs. The pipeline is built on the top of [bert_sequence_classifier_vop_drug_side_effect](https://nlp.johnsnowlabs.com/2023/06/13/bert_sequence_classifier_vop_drug_side_effect_en.html) model. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_vop_drug_side_effect_pipeline_en_4.4.3_3.2_1686704779005.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_vop_drug_side_effect_pipeline_en_4.4.3_3.2_1686704779005.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("bert_sequence_classifier_vop_drug_side_effect_pipeline", "en", "clinical/models") + +pipeline.annotate("I felt kind of dizzy after taking that medication for a month.") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("bert_sequence_classifier_vop_drug_side_effect_pipeline", "en", "clinical/models") + +val result = pipeline.annotate(I felt kind of dizzy after taking that medication for a month.) +``` +
+ +## Results + +```bash +| text | prediction | +|:---------------------------------------------------------------|:-------------| +| I felt kind of dizzy after taking that medication for a month. | Drug_AE | + +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_vop_drug_side_effect_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.3+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|406.4 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- MedicalBertForSequenceClassification diff --git a/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md b/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md new file mode 100644 index 0000000000..72400ce0f0 --- /dev/null +++ b/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md @@ -0,0 +1,77 @@ +--- +layout: model +title: HCP Consult Classification Pipeline - Voice of the Patient +author: John Snow Labs +name: bert_sequence_classifier_vop_hcp_consult_pipeline +date: 2023-06-14 +tags: [licensed, en, clinical, classification, vop] +task: Text Classification +language: en +edition: Healthcare NLP 4.4.3 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline includes the Medical Bert for Sequence Classification model to identify texts that mention a HCP consult. The pipeline is built on the top of [bert_sequence_classifier_vop_hcp_consult](https://nlp.johnsnowlabs.com/2023/06/13/bert_sequence_classifier_vop_hcp_consult_en.html) model. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_vop_hcp_consult_pipeline_en_4.4.3_3.2_1686708308086.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_vop_hcp_consult_pipeline_en_4.4.3_3.2_1686708308086.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("bert_sequence_classifier_vop_hcp_consult_pipeline", "en", "clinical/models") + +pipeline.annotate("My son has been to two doctors who gave him antibiotic drops but they also say the problem might related to allergies.") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("bert_sequence_classifier_vop_hcp_consult_pipeline", "en", "clinical/models") + +val result = pipeline.annotate(My son has been to two doctors who gave him antibiotic drops but they also say the problem might related to allergies.) +``` +
+ +## Results + +```bash +| text | prediction | +|:-----------------------------------------------------------------------------------------------------------------------|:-----------------| +| My son has been to two doctors who gave him antibiotic drops but they also say the problem might related to allergies. | Consulted_By_HCP | + +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_vop_hcp_consult_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.3+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|406.4 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- MedicalBertForSequenceClassification diff --git a/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_self_report_pipeline_en.md b/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_self_report_pipeline_en.md new file mode 100644 index 0000000000..f7d824875b --- /dev/null +++ b/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_self_report_pipeline_en.md @@ -0,0 +1,77 @@ +--- +layout: model +title: Self Report Classification Pipeline - Voice of the Patient +author: John Snow Labs +name: bert_sequence_classifier_vop_self_report_pipeline +date: 2023-06-14 +tags: [licensed, en, clinical, vop, classification] +task: Text Classification +language: en +edition: Healthcare NLP 4.4.3 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline includes the Medical Bert for Sequence Classification model to classify texts depending on if they are self-reported or if they refer to another person. The pipeline is built on the top of [bert_sequence_classifier_vop_self_report](https://nlp.johnsnowlabs.com/2023/06/13/bert_sequence_classifier_vop_self_report_en.html) model. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_vop_self_report_pipeline_en_4.4.3_3.2_1686702483761.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_vop_self_report_pipeline_en_4.4.3_3.2_1686702483761.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("bert_sequence_classifier_vop_self_report_pipeline", "en", "clinical/models") + +pipeline.annotate("My friend was treated for her skin cancer two years ago.") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("bert_sequence_classifier_vop_self_report_pipeline", "en", "clinical/models") + +val result = pipeline.annotate(My friend was treated for her skin cancer two years ago.) +``` +
+ +## Results + +```bash +| text | prediction | +|:---------------------------------------------------------|:-------------| +| My friend was treated for her skin cancer two years ago. | 3rd_Person | + +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_vop_self_report_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.3+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|406.4 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- MedicalBertForSequenceClassification diff --git a/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_sound_medical_pipeline_en.md b/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_sound_medical_pipeline_en.md new file mode 100644 index 0000000000..e429a5b39b --- /dev/null +++ b/docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_sound_medical_pipeline_en.md @@ -0,0 +1,76 @@ +--- +layout: model +title: Sound Medical Classification Pipeline - Voice of the Patient +author: John Snow Labs +name: bert_sequence_classifier_vop_sound_medical_pipeline +date: 2023-06-14 +tags: [licensed, en, clinical, classification, vop] +task: Text Classification +language: en +edition: Healthcare NLP 4.4.3 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline includes the Medical Bert for Sequence Classification model to identify whether the suggestion that is mentioned in the text is medically sound. The pipeline is built on the top of [bert_sequence_classifier_vop_sound_medical](https://nlp.johnsnowlabs.com/2023/06/13/bert_sequence_classifier_vop_sound_medical_en.html) model. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_vop_sound_medical_pipeline_en_4.4.3_3.2_1686710496292.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_vop_sound_medical_pipeline_en_4.4.3_3.2_1686710496292.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline("bert_sequence_classifier_vop_sound_medical_pipeline", "en", "clinical/models") + +pipeline.annotate("I had a lung surgery for emphyema and after surgery my xray showing some recovery.") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("bert_sequence_classifier_vop_sound_medical_pipeline", "en", "clinical/models") + +val result = pipeline.annotate(I had a lung surgery for emphyema and after surgery my xray showing some recovery.) +``` +
+ +## Results + +```bash +| text | prediction | +|:-----------------------------------------------------------------------------------|:-------------| +| I had a lung surgery for emphyema and after surgery my xray showing some recovery. | True | +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_vop_sound_medical_pipeline| +|Type:|pipeline| +|Compatibility:|Healthcare NLP 4.4.3+| +|License:|Licensed| +|Edition:|Official| +|Language:|en| +|Size:|406.4 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- MedicalBertForSequenceClassification