Relation Extraction Between Body Parts and Problem Entities (ReDL)

Description

Relation extraction between body parts and problem entities in clinical texts. 1 : Shows that there is a relation between body part entity and entities labeled as problem ( diagnosis, symptom etc.), 0 : Shows that there no relation between body part and problem entities.

Predicted Entities

0, 1

Open in Colab Download Copy S3 URI

How to use

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

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

tokenizer = Tokenizer()\
    .setInputCols(["sentences"])\
    .setOutputCol("tokens")

pos_tagger = PerceptronModel()\
    .pretrained("pos_clinical", "en", "clinical/models") \
    .setInputCols(["sentences", "tokens"])\
    .setOutputCol("pos_tags")

words_embedder = WordEmbeddingsModel() \
    .pretrained("embeddings_clinical", "en", "clinical/models") \
    .setInputCols(["sentences", "tokens"]) \
    .setOutputCol("embeddings")

ner_tagger = MedicalNerModel.pretrained("ner_jsl_greedy", "en", "clinical/models")\
    .setInputCols("sentences", "tokens", "embeddings")\
    .setOutputCol("ner_tags") 

ner_converter = NerConverterInternal() \
    .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',"EXTERNAL_BODY_PART_OR_REGION-SYMPTOM"])

# 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_bodypart_problem_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 ="No neurologic deficits other than some numbness in his left hand."

data = spark.createDataFrame([[text]]).toDF("text")

result = pipeline.fit(data).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 pos_tagger = PerceptronModel()
    .pretrained("pos_clinical", "en", "clinical/models") 
    .setInputCols(Array("sentences", "tokens"))
    .setOutputCol("pos_tags")

val words_embedder = WordEmbeddingsModel()
    .pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentences", "tokens"))
    .setOutputCol("embeddings")

val ner_tagger = MedicalNerModel.pretrained("ner_jsl_greedy", "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 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("SYMPTOM-EXTERNAL_BODY_PART_OR_REGION","EXTERNAL_BODY_PART_OR_REGION-SYMPTOM"))

// 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_bodypart_problem_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("No neurologic deficits other than some numbness in his left hand.").toDS.toDF("text")

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

import nlu
nlu.load("en.relation.bodypart.problem").predict("""No neurologic deficits other than some numbness in his left hand.""")

Results

+--------+-------+-------------------+----------------------------+------+----------+
|relation|entity1|chunk1             |entity2                     |chunk2|confidence|
+--------+-------+-------------------+----------------------------+------+----------+
|0       |Symptom|neurologic deficits|External_body_part_or_region|hand  |0.8320218 |
|1       |Symptom|numbness           |External_body_part_or_region|hand  |0.99943227|
+--------+-------+-------------------+----------------------------+------+----------+

Model Information

Model Name:	redl_bodypart_problem_biobert
Compatibility:	Healthcare NLP 4.2.4+
License:	Licensed
Edition:	Official
Language:	en
Size:	401.7 MB

References

Trained on internal dataset.

Benchmarking

label              Recall Precision        F1   Support
0                   0.762     0.814     0.787       315
1                   0.938     0.917     0.927       885
Avg.                0.850     0.865     0.857        -

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