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
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.""")
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 |