Relation extraction between Drugs and ADE (ReDL)

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 Download

How to use

...
documenter = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentencer = SentenceDetector()\
    .setInputCols(["document"])\
    .setOutputCol("sentences")

tokenizer = sparknlp.annotators.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 = NerConverter() \
    .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 = sparknlp.annotators.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])

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"""

p_model = pipeline.fit(spark.createDataFrame([[text]]).toDF("text"))

result = p_model.transform(data)
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 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")

// Set a filter on pairs of named entities which will be treated as relation candidates
val re_ner_chunk_filter = 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, pos_tagger, words_embedder, ner_tagger, ner_converter, dependency_parser, re_ner_chunk_filter, re_model))

val data = Seq("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").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)

Results

|    | 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: redl_ade_biobert
Compatibility: Spark NLP for Healthcare 3.1.2+
License: Licensed
Edition: Official
Input Labels: [redl_ner_chunks, document]
Output Labels: [relations]
Language: en

Data Source

This model is trained on custom data annotated by JSL.

Benchmarking

Relation           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      -