Description
A pipeline for Adverse Drug Events (ADE) with ner_ade_biobert, assertiondl_biobert and classifierdl_ade_conversational_biobert. It will extract ADE and DRUG clinical entities, assign assertion status to ADE entities, and then assign ADE status to a text (True means ADE, False means not related to ADE).
Live Demo Open in Colab Copy S3 URI
How to use
pipeline = PretrainedPipeline('explain_clinical_doc_ade', 'en', 'clinical/models')
res = pipeline.fullAnnotate('The clinical course suggests that the interstitial pneumonitis was induced by hydroxyurea.')
val era_pipeline = new PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
val result = era_pipeline.fullAnnotate("""The clinical course suggests that the interstitial pneumonitis was induced by hydroxyurea.""")(0)
import nlu
nlu.load("en.explain_doc.clinical_ade").predict("""The clinical course suggests that the interstitial pneumonitis was induced by hydroxyurea.""")
Results
| # | chunks | entities | assertion |
|----|-------------------------------|------------|------------|
| 0 | interstitial pneumonitis | ADE | Present |
Model Information
| Model Name: | explain_clinical_doc_ade |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
Included Models
- DocumentAssembler
- TokenizerModel
- BertEmbeddings
- MedicalNerModel
- NerConverter
- NerConverter
- AssertionDLModel
- SentenceEmbeddings
- ClassifierDLModel