Description
A pipeline for Adverse Drug Events (ADE) with ner_ade_biobert, assertion_dl_biobert, classifierdl_ade_conversational_biobert, and re_ade_biobert . It will classify the document, extract ADE and DRUG clinical entities, assign assertion status to ADE entities, and relate Drugs with their ADEs.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("explain_clinical_doc_ade", "en", "clinical/models")
text = """Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"""
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("explain_clinical_doc_ade", "en", "clinical/models")
val text = """Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"""
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.explain_doc.clinical_ade").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps""")
Results
Class: True
NER_Assertion:
| | chunk | entitiy | assertion |
|----|-------------------------|------------|-------------|
| 0 | Lipitor | DRUG | - |
| 1 | severe fatigue | ADE | Conditional |
| 2 | voltaren | DRUG | - |
| 3 | cramps | ADE | Conditional |
Relations:
| | chunk1 | entitiy1 | chunk2 | entity2 | relation |
|----|-------------------------------|------------|-------------|---------|----------|
| 0 | severe fatigue | ADE | Lipitor | DRUG | 1 |
| 1 | cramps | ADE | Lipitor | DRUG | 0 |
| 2 | severe fatigue | ADE | voltaren | DRUG | 0 |
| 3 | cramps | ADE | voltaren | DRUG | 1 |
Model Information
| Model Name: | explain_clinical_doc_ade |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 485.0 MB |
Included Models
- DocumentAssembler
- TokenizerModel
- BertEmbeddings
- SentenceEmbeddings
- ClassifierDLModel
- MedicalNerModel
- NerConverterInternalModel
- PerceptronModel
- DependencyParserModel
- RelationExtractionModel
- NerConverterInternalModel
- AssertionDLModel