Temporal relations for clinical events

Description

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

Included Relations

AFTER, BEFORE, OVERLAP

Live Demo Open in ColabDownload

How to use

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


clinical_re_Model = RelationExtractionModel()\
    .pretrained("re_temporal_events_clinical", "en", 'clinical/models')\
    .setInputCols(["embeddings", "pos_tags", "ner_chunks", "dependencies"])\
    .setOutputCol("relations")\
    .setMaxSyntacticDistance(4)\ #default: 0
    .setPredictionThreshold(0.9)\ #default: 0.5
    .setRelationPairs(["date-problem", "occurrence-date"]) # Possible relation pairs. Default: All Relations.

nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos_tagger, dependecy_parser, word_embeddings, clinical_ner, ner_converter, 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.""")

Results

+----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
|    | relation   | entity1    |   entity1_begin |   entity1_end | chunk1                   | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
+====+============+============+=================+===============+==========================+===========+=================+===============+=====================+==============+
|  0 | OVERLAP    | OCCURRENCE |             121 |           144 | a motor vehicle accident | DATE      |             149 |           165 | September of 2005   |     0.999975 |
+----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
|  1 | OVERLAP    | DATE       |             171 |           179 | that time                | PROBLEM   |             201 |           219 | any specific injury |     0.956654 |
+----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+

Model Information

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