2023-06-13-bert_sequence_classifier_vop_side_effect_pipeline_en (#380)

docs/_posts/mauro-nievoff/2023-06-13-bert_sequence_classifier_vop_side_effect_pipeline_en.md

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+---
+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}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[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.
+
+<div class="tabs-box" markdown="1">
+{% 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.)
+```
+</div>
+
+## 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

docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_drug_side_effect_pipeline_en.md

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+---
+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}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[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
+
+
+
+<div class="tabs-box" markdown="1">
+{% 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.)
+```
+</div>
+
+## 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

docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md

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+---
+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}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[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
+
+
+
+<div class="tabs-box" markdown="1">
+{% 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.)
+```
+</div>
+
+## 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

docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_self_report_pipeline_en.md

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+---
+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}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[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
+
+
+
+<div class="tabs-box" markdown="1">
+{% 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.)
+```
+</div>
+
+## 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

docs/_posts/mauro-nievoff/2023-06-14-bert_sequence_classifier_vop_sound_medical_pipeline_en.md

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+---
+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}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[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
+
+
+
+<div class="tabs-box" markdown="1">
+{% 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.)
+```
+</div>
+
+## 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