Detect Temporal Relations for Clinical Events (Enriched)

Description

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

Included Relations

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

Live Demo Open in Colab Download

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_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, 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.""")

...

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, dependecy_parser, word_embeddings, clinical_ner, ner_converter, clinical_re_Model))

val result = pipeline.fit(Seq.empty["""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")).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: 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

Data Source

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

Benchmarking

```bash |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 |