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. It is based on ‘biobert_pubmed_base_cased’ embeddings. 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
documenter = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentencer = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentences")
tokenizer = Tokenizer()\
.setInputCols(["sentences"])\
.setOutputCol("tokens")
embedder = BertEmbeddings.pretrained("biobert_pubmed_base_cased", "en")\
.setInputCols(["sentences", "tokens"]) \
.setOutputCol("embeddings")
pos_tagger = PerceptronModel()\
.pretrained("pos_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens"])\
.setOutputCol("pos_tags")
ner_tagger = MedicalNerModel() \
.pretrained("ner_ade_biobert", "en", "clinical/models") \
.setInputCols(["sentences", "tokens", "embeddings"]) \
.setOutputCol("ner_tags")
ner_chunker = NerConverter() \
.setInputCols(["sentences", "tokens", "ner_tags"]) \
.setOutputCol("ner_chunks")
dependency_parser = sparknlp.annotators.DependencyParserModel()\
.pretrained("dependency_conllu", "en")\
.setInputCols(["sentences", "pos_tags", "tokens"])\
.setOutputCol("dependencies")
re_model = RelationExtractionModel()\
.pretrained("re_ade_biobert", "en", 'clinical/models')\
.setInputCols(["embeddings", "pos_tags", "ner_chunks", "dependencies"])\
.setOutputCol("relations")\
.setMaxSyntacticDistance(3)\
.setPredictionThreshold(0.5)\
.setRelationPairs(["ade-drug", "drug-ade"])
nlp_pipeline = Pipeline(stages=[documenter, sentencer, tokenizer, embedder, pos_tagger, ner_tagger, ner_chunker, dependency_parser, re_model])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")))
text ="""Been taking Lipitor for 15 years , have experienced sever fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"""
annotations = light_pipeline.fullAnnotate(text)
val documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentencer = new SentenceDetector()
.setInputCols("document")
.setOutputCol("sentences")
val tokenizer = new Tokenizer()
.setInputCols("sentences")
.setOutputCol("tokens")
val embedder = BertEmbeddings.pretrained("biobert_pubmed_base_cased", "en").pretrained("biobert_pubmed_base_cased", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("embeddings")
val ner_tagger = MEdicalNerModel()
.pretrained("ner_ade_biobert", "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_biobert", "en", 'clinical/models')
.setInputCols(Array("embeddings", "pos_tags", "ner_chunks", "dependencies"))
.setOutputCol("relations")
.setMaxSyntacticDistance(3) #default: 0
.setPredictionThreshold(0.5) #default: 0.5
.setRelationPairs(Array("drug-ade", "ade-drug")) # Possible relation pairs. Default: All Relations.
val nlpPipeline = new Pipeline().setStages(Array(documenter, sentencer, tokenizer, embedder, pos_tagger, ner_tagger, ner_chunker, dependency_parser, re_model))
val data = Seq("""Been taking Lipitor for 15 years , have experienced sever fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps""").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.relation.ade_biobert").predict("""Been taking Lipitor for 15 years , have experienced sever fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps""")
Results
| | chunk1 | entitiy1 | chunk2 | entity2 | relation |
|----|-------------------------------|------------|-------------|---------|----------|
| 0 | sever fatigue | ADE | Lipitor | DRUG | 1 |
| 1 | cramps | ADE | Lipitor | DRUG | 0 |
| 2 | sever fatigue | ADE | voltaren | DRUG | 0 |
| 3 | cramps | ADE | voltaren | DRUG | 1 |
Model Information
| Model Name: | re_ade_biobert |
| 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 0.91 0.92 0.92 1670
1 0.92 0.91 0.91 1673
micro-avg 0.92 0.92 0.92 3343
macro-avg 0.92 0.92 0.92 3343
weighted-avg 0.92 0.92 0.92 3343