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  • John Snow Labs
  • Sep 28, 2020

Detect Temporal Relations for Clinical Events (Enriched)

  • re
  • en
  • clinical
  • licensed

Description

This model can be used to identify temporal relationships among clinical events.

Predicted Entities

BEFORE, AFTER, SIMULTANEOUS, BEGUN_BY, ENDED_BY, DURING, BEFORE_OVERLAP

Live Demo Open in Colab Copy S3 URI

How to use

Use as part of an nlp pipeline with the following stages: DocumentAssembler, SentenceDetector, Tokenizer, PerceptronModel, DependencyParserModel, WordEmbeddingsModel, NerDLModel, NerConverter, RelationExtractionModel.

In the table below, re_temporal_events_enriched_clinical RE model, its labels, optimal NER model, and meaningful relation pairs are illustrated.

RE MODEL RE MODEL LABES NER MODEL RE PAIRS
re_temporal_events_enriched_clinical BEFORE, AFTER, SIMULTANEOUS, BEGUN_BY, ENDED_BY, DURING, BEFORE_OVERLAP ner_events_clinical [“No need to set pairs.”] 
document_assembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

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

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

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

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

clinical_ner = MedicalNerModel.pretrained("ner_clinical", "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")

clinical_re_Model = RelationExtractionModel()\
    .pretrained("re_temporal_events_enriched_clinical", "en", 'clinical/models')\
    .setInputCols(["embeddings", "pos_tags", "ner_chunks", "dependencies"])\
    .setOutputCol("relations")\
    .setMaxSyntacticDistance(4) #default: 0
    
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos_tagger, word_embeddings, clinical_ner, ner_converter,  dependency_parser, clinical_re_Model])

light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))

annotations = light_pipeline.fullAnnotate("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")


val document_assembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

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

val clinical_ner = MedicalNerModel.pretrained("ner_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 dependency_parser = DependencyParserModel().pretrained("dependency_conllu", "en")
    .setInputCols(Array("sentences", "pos_tags", "tokens"))
    .setOutputCol("dependencies")

val clinical_re_Model = RelationExtractionModel()
    .pretrained("re_temporal_events_enriched_clinical", "en", "clinical/models")
    .setInputCols("embeddings", "pos_tags", "ner_chunks", "dependencies")
    .setOutputCol("relations")
    .setMaxSyntacticDistance(4)

val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos_tagger, word_embeddings, clinical_ner, ner_converter,  dependency_parser, clinical_re_Model))

val data = Seq("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""").toDS().toDF("text")

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

Results

+----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
|    | relation   | entity1   |   entity1_begin |   entity1_end | chunk1                                        | entity2    |   entity2_begin |   entity2_end | chunk2                   |   confidence |
+====+============+===========+=================+===============+===============================================+============+=================+===============+==========================+==============+
|  0 | OVERLAP    | PROBLEM   |              54 |            98 | longstanding intermittent right low back pain | OCCURRENCE |             121 |           144 | a motor vehicle accident |     0.532308 |
+----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
|  1 | AFTER      | DATE      |             171 |           179 | that time                                     | PROBLEM    |             201 |           219 | any specific injury      |     0.577288 |
+----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+

Model Information

Model Name: re_temporal_events_enriched_clinical
Type: re
Compatibility: Healthcare NLP 2.6.0 +
Edition: Official
License: Licensed
Input Labels: [embeddings, pos_tags, ner_chunks, dependencies]
Output Labels: [relations]
Language: [en]
Case sensitive: false

Data Source

Trained on data gathered and manually annotated by John Snow Labs https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/

Benchmarking

|Relation  | Recall  | Precision | F1   |
|---------:|--------:|----------:|-----:|
| OVERLAP  |  0.81   |  0.73     | 0.77 |
| BEFORE   |  0.85   |  0.88     | 0.86 |
| AFTER    |  0.38   |  0.46     | 0.43 |