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 |