Extract relations between effects of using multiple drugs (ReDL)

Description

Extract potential improvements or harmful effects of Drug-Drug interactions (DDIs) when two or more drugs are taken at the same time or at certain interval

Predicted Entities

DDI-advise, DDI-effect, DDI-false, DDI-int, DDI-mechanism

Open in Colab Download

How to use

...
words_embedder = WordEmbeddingsModel() \
    .pretrained("embeddings_clinical", "en", "clinical/models") \
    .setInputCols(["sentences", "tokens"]) \
    .setOutputCol("embeddings")
ner_tagger = NerDLModel() \
    .pretrained("ner_posology", "en", "clinical/models") \
    .setInputCols(["sentences", "tokens", "embeddings"]) \
    .setOutputCol("ner_tags")
ner_converter = NerConverter() \
    .setInputCols(["sentences", "tokens", "ner_tags"]) \
    .setOutputCol("ner_chunks")
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(['SYMPTOM-EXTERNAL_BODY_PART_OR_REGION'])

# 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_drug_drug_interaction_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text="""When carbamazepine is withdrawn from the combination therapy, aripiprazole dose should then be reduced. \
If additional adrenergic drugs are to be administered by any route, \
they should be used with caution because the pharmacologically predictable sympathetic effects of Metformin may be potentiated"""

data = spark.createDataFrame([[text]]).toDF("text")
p_model = pipeline.fit(data)
result = p_model.transform(data)
...
val words_embedder = WordEmbeddingsModel()
    .pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentences", "tokens"))
    .setOutputCol("embeddings")
val ner_tagger = NerDLModel()
    .pretrained("ner_clinical", "en", "clinical/models")
    .setInputCols(Array("sentences", "tokens", "embeddings"))
    .setOutputCol("ner_tags")
val ner_converter = NerConverter()
    .setInputCols(Array("sentences", "tokens", "ner_tags"))
    .setOutputCol("ner_chunks")
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('SYMPTOM-EXTERNAL_BODY_PART_OR_REGION'))

// 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_drug_drug_interaction_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 result = pipeline.fit(Seq.empty["When carbamazepine is withdrawn from the combination therapy, aripiprazole dose should then be reduced. If additional adrenergic drugs are to be administered by any route, they should be used with caution because the pharmacologically predictable sympathetic effects of Metformin may be potentiated"].toDS.toDF("text")).transform(data)

Results

|    | relation   | entity1   |   entity1_begin |   entity1_end | chunk1        | entity2   |   entity2_begin |   entity2_end | chunk2       |   confidence |
|---:|:-----------|:----------|----------------:|--------------:|:--------------|:----------|----------------:|--------------:|:-------------|-------------:|
|  0 | DDI-advise | DRUG      |               5 |            17 | carbamazepine | DRUG      |              62 |            73 | aripiprazole |      0.99238 |

Model Information

Model Name: redl_drug_drug_interaction_biobert
Compatibility: Spark NLP 2.7.3+
License: Licensed
Edition: Official
Language: en

Data Source

Trained on DDI Extraction corpus.

Benchmarking

Relation           Recall Precision        F1   Support
DDI-advise          0.758     0.874     0.812       211
DDI-effect          0.759     0.754     0.756       348
DDI-false           0.977     0.957     0.967      4097
DDI-int             0.175     0.458     0.253        63
DDI-mechanism       0.783     0.853     0.816       281
Avg.                0.690     0.779     0.721