Description
This model is an end-to-end trained BioBERT model, 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
documenter = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentencer = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentences")
tokenizer = Tokenizer()\
.setInputCols(["sentences"])\
.setOutputCol("tokens")
words_embedder = WordEmbeddingsModel() \
.pretrained("embeddings_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens"]) \
.setOutputCol("embeddings")
ner_tagger = MedicalNerModel.pretrained("ner_ade_clinical", "en", "clinical/models")\
.setInputCols("sentences", "tokens", "embeddings")\
.setOutputCol("ner_tags")
ner_converter = NerConverterInternal() \
.setInputCols(["sentences", "tokens", "ner_tags"]) \
.setOutputCol("ner_chunks")
pos_tagger = PerceptronModel()\
.pretrained("pos_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens"])\
.setOutputCol("pos_tags")
dependency_parser = DependencyParserModel()\
.pretrained("dependency_conllu", "en")\
.setInputCols(["sentences", "pos_tags", "tokens"])\
.setOutputCol("dependencies")
# Set a filter on pairs of named entities which will be treated as relation candidates
re_ner_chunk_filter = RENerChunksFilter() \
.setInputCols(["ner_chunks", "dependencies"])\
.setMaxSyntacticDistance(10)\
.setOutputCol("re_ner_chunks")\
.setRelationPairs(['ade-drug', 'drug-ade'])
# The dataset this model is trained to is sentence-wise.
# This model can also be trained on document-level relations - in which case, while predicting, use "document" instead of "sentence" as input.
re_model = RelationExtractionDLModel()\
.pretrained('redl_ade_biobert', 'en', "clinical/models") \
.setPredictionThreshold(0.5)\
.setInputCols(["re_ner_chunks", "sentences"]) \
.setOutputCol("relations")
pipeline = Pipeline(stages=[documenter,
sentencer,
tokenizer,
pos_tagger,
words_embedder,
ner_tagger,
ner_converter,
dependency_parser,
re_ner_chunk_filter,
re_model])
light_pipeline = LightPipeline(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 documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentencer = 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 = MedicalNerModel.pretrained("ner_ade_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens", "embeddings"))
.setOutputCol("ner_tags")
val ner_converter = new NerConverterInternal()
.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")
// Set a filter on pairs of named entities which will be treated as relation candidates
val re_ner_chunk_filter = new RENerChunksFilter()
.setInputCols(Array("ner_chunks", "dependencies"))
.setMaxSyntacticDistance(10)
.setOutputCol("re_ner_chunks")
.setRelationPairs(Array("drug-ade", "ade-drug"))
// The dataset this model is trained to is sentence-wise.
// This model can also be trained on document-level relations - in which case, while predicting, use "document" instead of "sentence" as input.
val re_model = RelationExtractionDLModel()
.pretrained("redl_ade_biobert", "en", "clinical/models")
.setPredictionThreshold(0.5)
.setInputCols(Array("re_ner_chunks", "sentences"))
.setOutputCol("relations")
val pipeline = new Pipeline().setStages(Array(documenter,
sentencer,
tokenizer,
words_embedder,
ner_tagger,
ner_converter,
pos_tagger,
dependency_parser,
re_ner_chunk_filter,
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 = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.relation.ade").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 | 0.998156 |
| 1 | DRUG | 97 | 105 | voltarene | ADE | 144 | 156 | muscle cramps | 0.985513 |
Model Information
| Model Name: | redl_ade_biobert |
| Compatibility: | Healthcare NLP 4.2.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 401.7 MB |
References
This model is trained on custom data annotated by JSL.
Benchmarking
label Recall Precision F1 Support
0 0.829 0.895 0.861 1146
1 0.955 0.923 0.939 2454
Avg. 0.892 0.909 0.900 -
Weighted-Avg. 0.915 0.914 0.914 -