Description
This model is capable of Relating Drugs and adverse reactions caused by them; It predicts if an adverse event is caused by a drug or not. 1 : Shows the adverse event and drug entities are related, 0 : Shows the adverse event and drug entities are not related.
Predicted Entities
0, 1
Live Demo Open in Colab Copy S3 URI
How to use
In the table below, re_ade_clinical RE model, its labels, optimal NER model, and meaningful relation pairs are illustrated.
| RE MODEL | RE MODEL LABES | NER MODEL | RE PAIRS |
|---|---|---|---|
| re_ade_clinical | 0 1 |
ner_ade_clinical | [“ade-drug”, “drug-ade”] |
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl","xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
words_embedder = WordEmbeddingsModel() \
.pretrained("embeddings_clinical", "en", "clinical/models") \
.setInputCols(["sentence", "token"]) \
.setOutputCol("embeddings")
ner_tagger = MedicalNerModel() \
.pretrained("ner_ade_clinical", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner_tags")
ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "ner_tags"]) \
.setOutputCol("ner_chunks")
pos_tagger = PerceptronModel()\
.pretrained("pos_clinical", "en", "clinical/models") \
.setInputCols(["sentence", "token"])\
.setOutputCol("pos_tags")
dependency_parser = sparknlp.annotators.DependencyParserModel()\
.pretrained("dependency_conllu", "en")\
.setInputCols(["sentence", "pos_tags", "token"])\
.setOutputCol("dependencies")
re_model = RelationExtractionModel()\
.pretrained("re_ade_clinical", "en", 'clinical/models')\
.setInputCols(["embeddings", "pos_tags", "ner_chunks", "dependencies"])\
.setOutputCol("relations")\
.setMaxSyntacticDistance(10)\
.setPredictionThreshold(0.1)\
.setRelationPairs(["ade-drug", "drug-ade"])\
.setRelationPairsCaseSensitive(False)
nlp_pipeline = Pipeline(stages=[documentAssembler,
sentenceDetector,
tokenizer,
words_embedder,
ner_tagger,
ner_converter,
pos_tagger,
dependency_parser,
re_model])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")))
text ="""Been taking Lipitor for 15 years , have experienced severe fatigue a lot. The doctor moved me to voltarene 2 months ago, so far I have only had muscle cramps. """
annotations = light_pipeline.fullAnnotate(text)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.setInputCols("document")
.setOutputCol("sentences")
val tokenizer = new Tokenizer()
.setInputCols("sentences")
.setOutputCol("tokens")
val words_embedder = WordEmbeddingsModel()
.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("embeddings")
val ner_tagger = NerDLModel()
.pretrained("ner_ade_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens", "embeddings"))
.setOutputCol("ner_tags")
val ner_converter = new NerConverter()
.setInputCols(Array("sentences", "tokens", "ner_tags"))
.setOutputCol("ner_chunks")
val pos_tagger = PerceptronModel()
.pretrained("pos_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("pos_tags")
val dependency_parser = DependencyParserModel()
.pretrained("dependency_conllu", "en")
.setInputCols(Array("sentences", "pos_tags", "tokens"))
.setOutputCol("dependencies")
val re_model = RelationExtractionModel()
.pretrained("re_ade_clinical", "en", 'clinical/models')
.setInputCols(Array("embeddings", "pos_tags", "ner_chunks", "dependencies"))
.setOutputCol("relations")
.setMaxSyntacticDistance(3)
.setPredictionThreshold(0.5)
.setRelationPairs(Array("drug-ade", "ade-drug"))
val nlpPipeline = new Pipeline().setStages(Array(
documentAssembler,
sentenceDetector,
tokenizer,
words_embedder,
ner_tagger,
ner_converter,
pos_tagger,
dependency_parser,
re_model))
val data = Seq("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot. The doctor moved me to voltarene 2 months ago, so far I have only had muscle cramps. """).toDS.toDF("text")
val result = nlpPipeline.fit(data).transform(data)
import nlu
nlu.load("en.relation.adverse_drug_events.clinical").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot. The doctor moved me to voltarene 2 months ago, so far I have only had muscle cramps.""")
Results
| relation | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_begin | entity2_end | chunk2 | confidence |
|---------:|:--------|--------------:|------------:|:----------|:--------|--------------:|------------:|:---------------|-----------:|
| 1 | DRUG | 12 | 18 | Lipitor | ADE | 52 | 65 | severe fatigue | 1 |
| 1 | DRUG | 97 | 105 | voltarene | ADE | 144 | 156 | muscle cramps | 0.997283 |
Model Information
| Model Name: | re_ade_clinical |
| Type: | re |
| Compatibility: | Healthcare NLP 3.1.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [embeddings, pos_tags, train_ner_chunks, dependencies] |
| Output Labels: | [relations] |
| Language: | en |
Data Source
This model is trained on custom data annotated by JSL.
Benchmarking
label precision recall f1-score support
0 0.86 0.88 0.87 1787
1 0.92 0.90 0.91 2586
micro-avg 0.89 0.89 0.89 4373
macro-avg 0.89 0.89 0.89 4373
weighted-avg 0.89 0.89 0.89 4373