added nlu to modelshub cards (#212)

docs/_posts/2020-01-31-clinical_deidentification_en.md

@@ -36,7 +36,7 @@ This pipeline can be used to de-identify PHI information from medical texts. The
 
 <div class="tabs-box" markdown="1">
 
-{% include programmingLanguageSelectScalaPython.html %}
+{% include programmingLanguageSelectScalaPythonNLU.html %}
 
 ```python
 from sparknlp.pretrained import PretrainedPipeline
@@ -64,6 +64,18 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 val model = new PretrainedPipeline("clinical_deidentification", "en", "clinical/models")
 val result = deid_pipeline.annotate(sample)
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.de_identify.clinical_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.""")
+```
+
 </div>
 
 {:.model-param}

docs/_posts/2020-09-28-ner_healthcare_de.md

@@ -31,7 +31,7 @@ Use as part of an nlp pipeline with the following stages: DocumentAssembler, Sen
 
 <div class="tabs-box" markdown="1">
 
-{% include programmingLanguageSelectScalaPython.html %}
+{% include programmingLanguageSelectScalaPythonNLU.html %}
 
 
 ```python
@@ -64,6 +64,14 @@ val result = pipeline.fit(data).transform(data)
 
 ```
 
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("de.med_ner.healthcare").predict("""Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist ein hochmalignes bronchogenes Karzinom""")
+```
+
 </div>
 
 {:.h2_title}

docs/_posts/Ahmetemintek/2022-02-26-sbiobertresolve_snomed_findings_aux_concepts_en_3_0.md

@@ -148,6 +148,14 @@ val df = Seq(text).toDF(“text”)
 
 val result= nlpPipeline.fit(df).transform(df)
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.resolve.snomed.findings_aux_concepts").predict("""FINDINGS: The patient was found upon excision of the cyst that it contained a large Prolene suture; beneath this was a very small incisional hernia, the hernia cavity, which contained omentum; the hernia was easily repaired""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-05-27-rct_binary_classifier_biobert_en_3_0.md

@@ -90,6 +90,14 @@ val data = Seq("""Abstract:Based on the American Society of Anesthesiologists' P
 
 val result = biobert_clf_pipeline.fit(data).transform(data)
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.classify.rct_binary_biobert").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. """)
+```
+
 </div>
 
 

docs/_posts/Ahmetemintek/2022-05-27-rct_binary_classifier_use_en_3_0.md

@@ -86,6 +86,14 @@ val data = Seq("""Abstract:Based on the American Society of Anesthesiologists' P
 
 val result = use_clf_pipeline.fit(data).transform(data)
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.classify.rct_binary_use").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. """)
+```
+
 </div>
 
 

docs/_posts/Ahmetemintek/2022-07-06-umls_clinical_drugs_mapper_en_3_0.md

@@ -133,6 +133,15 @@ val test_data = Seq("She was immediately given hydrogen peroxide 30 mg, and has
 
 val result = pipeline.fit(test_data).transform(test_data) 
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.umls_clinical_drugs_mapper").predict("""She was immediately given hydrogen peroxide 30 mg, and has been advised Neosporin Cream for 5 days. 
+               She has a history of taking magnesium hydroxide 100mg/1ml and metformin 1000 mg.""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-07-08-umls_clinical_findings_mapper_en_3_0.md

@@ -132,6 +132,14 @@ val test_data = Seq("A 28-year-old female with a history of obesity with BMI of
 
 val result = mapper_pipeline.fit(test_data).transform(test_data)
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.umls_clinical_findings_mapper").predict("""A 28-year-old female with a history of obesity with BMI of 33.5 kg/m2, presented with a one-week history of vomiting.""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-07-11-umls_disease_syndrome_mapper_en_3_0.md

@@ -131,6 +131,14 @@ val test_data = Seq("A 35-year-old male with a history of obesity and gestationa
 
 val result = mapper_pipeline.fit(test_data).transform(test_data) 
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.umls_disease_syndrome_mapper").predict("""A 35-year-old male with a history of obesity and gestational diabetes mellitus and acyclovir allergy""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-07-11-umls_drug_substance_mapper_en_3_0.md

@@ -131,6 +131,14 @@ val test_data = Seq("The patient was given  metformin, lenvatinib and lavender 7
 
 val result = mapper_pipeline.fit(test_data).transform(test_data) 
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.umls_drug_substance_mapper").predict("""The patient was given  metformin, lenvatinib and lavender 700 ml/ml""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-07-11-umls_major_concepts_mapper_en_3_0.md

@@ -131,6 +131,14 @@ val test_data = Seq("The patient complains of pustules after falling from stairs
 
 val result = mapper_pipeline.fit(test_data).transform(test_data) 
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.umls_major_concepts_mapper").predict("""The patient complains of pustules after falling from stairs. Also,  she has a history of quadriceps tendon rupture""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-07-25-umls_drug_substance_resolver_pipeline_en_3_0.md

@@ -45,6 +45,14 @@ import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 val pipeline= PretrainedPipeline("umls_drug_substance_resolver_pipeline", "en", "clinical/models")
 val pipeline.annotate("The patient was given  metformin, lenvatinib and Magnesium hydroxide 100mg/1ml")
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.umls_drug_substance_resolver").predict("""The patient was given  metformin, lenvatinib and Magnesium hydroxide 100mg/1ml""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-07-25-umls_major_concepts_resolver_pipeline_en_3_0.md

@@ -45,6 +45,14 @@ import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 val pipeline= PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
 val pipeline.annotate("The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician")
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.umls_major_concepts_resolver").predict("""The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-07-26-umls_clinical_findings_resolver_pipeline_en_3_0.md

@@ -45,6 +45,14 @@ import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 val pipeline= PretrainedPipeline("umls_clinical_findings_resolver_pipeline", "en", "clinical/models")
 val pipeline.annotate("HTG-induced pancreatitis associated with an acute hepatitis, and obesity")
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.umls_clinical_findings_resolver").predict("""HTG-induced pancreatitis associated with an acute hepatitis, and obesity""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-07-26-umls_disease_syndrome_resolver_pipeline_en_3_0.md

@@ -45,6 +45,14 @@ import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 val pipeline= PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
 val pipeline.annotate("A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria")
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.umls_disease_syndrome_resolver").predict("""A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-07-26-umls_drug_resolver_pipeline_en_3_0.md

@@ -45,6 +45,14 @@ import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 val pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
 val pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.umls_drug_resolver").predict("""The patient was given Adapin 10 MG, coumadn 5 mg""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-08-03-explain_clinical_doc_radiology_en_3_0.md

@@ -48,6 +48,14 @@ val pipeline = new PretrainedPipeline("explain_clinical_doc_radiology", "en", "c
 
 val result = pipeline.fullAnnotate("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")(0)
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.explain_doc.clinical_radiology.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-08-23-drug_ade_mapper_en.md

@@ -129,6 +129,15 @@ val data = Seq("The patient was prescribed 1000 mg fish oil and multivitamins. S
 
 val result= pipeline.fit(data).transform(data)
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.drug_ade").predict("""The patient was prescribed 1000 mg fish oil and multivitamins. 
+            She was discharged on zopiclone and ambrisentan""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-09-15-assertion_jsl_augmented_en.md

@@ -129,6 +129,14 @@ val result = nlpPipeline.fit(data).transform(data)
 
 
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.assert.jsl_augmented").predict("""Patient had a headache for the last 2 weeks, and appears anxious when she walks fast. No alopecia noted. She denies pain. Her father is paralyzed and it is a stressor for her. She was bullied by her boss and got antidepressant. We prescribed sleeping pills for her current insomnia""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-09-29-rxnorm_normalized_mapper_en.md

@@ -127,6 +127,14 @@ val data = Seq("The patient was given Zyrtec 10 MG, Adapin 10 MG Oral Capsule, S
 
 val result = mapper_pipeline.fit(data).transform(data) 
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.rxnorm_normalized").predict("""The patient was given Zyrtec 10 MG, Adapin 10 MG Oral Capsule, Septi-Soothe 0.5 Topical Spray""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-09-30-icd10_icd9_mapper_en.md

@@ -104,6 +104,14 @@ val result= pipeline.fit(data).transform(data)
 
 
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.icd10_ic9").predict("""Diabetes Mellitus""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-09-30-icd10_icd9_mapping_en.md

@@ -47,6 +47,14 @@ val pipeline= new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/model
 
 val result= pipeline.fullAnnotate('Z833 A0100 A000')
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.icd10_icd9.mapping").predict("""Z833 A0100 A000""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-09-30-icd9_icd10_mapper_en.md

@@ -89,6 +89,14 @@ val data = Seq("00322").toDS.toDF("text")
 
 val result= pipeline.fit(data).transform(data)
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.icd9_icd10").predict("""Put your text here.""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-09-30-icd9_mapper_en.md

@@ -90,6 +90,14 @@ val test_data = Seq("24 completed weeks of gestation").toDS.toDF("text")
 
 val result = mapper_pipeline.fit(test_data).transform(test_data) 
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.map_entity.icd9").predict("""24 completed weeks of gestation""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-09-30-icd9_resolver_pipeline_en.md

@@ -49,6 +49,14 @@ val med_resolver_pipeline = new PretrainedPipeline("icd9_resolver_pipeline", "en
 
 val result = med_resolver_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.resolve.icd9.pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")
+```
+
 </div>
 
 ## Results

docs/_posts/Ahmetemintek/2022-09-30-sbiobertresolve_icd9_en.md

@@ -141,6 +141,14 @@ val data = Seq("A 28-year-old female with a history of gestational diabetes mell
 val result = pipeline.fit(data).transform(data)
 
 ```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.resolve.ic9").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years """)
+```
+
 </div>
 
 ## Results