Description
Relation extraction between body parts entites [Internal_organ_or_component, External_body_part_or_region] and Direction entity in clinical texts. 1 : Shows there is a relation between the body part entity and the direction entity, 0 : Shows there is no relation between the body part entity and the direction entity.
Predicted Entities
0, 1
How to use
words_embedder = WordEmbeddingsModel()\
.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentences", "tokens"])\
.setOutputCol("embeddings")
ner_tagger = sparknlp.annotators.NerDLModel()\
.pretrained("jsl_ner_wip_greedy_clinical","en","clinical/models")\
.setInputCols("sentences", "tokens", "embeddings")\
.setOutputCol("ner_tags")
pair_list = ['direction-internal_organ_or_component', 'internal_organ_or_component-direction']
re_model = RelationExtractionModel().pretrained("re_bodypart_directions","en","clinical/models")\
.setInputCols(["embeddings", "pos_tags", "ner_chunks", "dependencies"])\
.setOutputCol("relations")\
.setMaxSyntacticDistance(4)\
.setRelationPairs(pair_list)
pipeline = Pipeline(stages=[documenter, sentencer, tokenizer, words_embedder, pos_tagger, ner_tagger, ner_chunker, dependency_parser, re_model])
model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = LightPipeline(model).fullAnnotate(''' MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia ''')
...
val words_embedder = WordEmbeddingsModel()
.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("embeddings")
val ner_tagger = sparknlp.annotators.NerDLModel()
.pretrained("jsl_ner_wip_greedy_clinical","en","clinical/models")
.setInputCols("sentences", "tokens", "embeddings")
.setOutputCol("ner_tags")
val pair_list = Array('direction-internal_organ_or_component', 'internal_organ_or_component-direction')
val re_model = RelationExtractionModel().pretrained("re_bodypart_directions","en","clinical/models")
.setInputCols(Array("embeddings", "pos_tags", "ner_chunks", "dependencies"))
.setOutputCol("relations")
.setMaxSyntacticDistance(4)
.setRelationPairs(pair_list)
val nlpPipeline = new Pipeline().setStages(Array(documenter, sentencer, tokenizer, words_embedder, pos_tagger, ner_tagger, ner_chunker, dependency_parser, re_model))
val result = pipeline.fit(Seq.empty[String]).transform(data)
Results
| index | relations | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2 | confidence |
|-------|-----------|-----------------------------|---------------|-------------|------------|-----------------------------|-------------|-------------|---------------|------------|
| 0 | 1 | Direction | 35 | 39 | upper | Internal_organ_or_component | 41 | 50 | brain stem | 0.9999989 |
| 1 | 0 | Direction | 35 | 39 | upper | Internal_organ_or_component | 59 | 68 | cerebellum | 0.99992585 |
| 2 | 0 | Direction | 35 | 39 | upper | Internal_organ_or_component | 81 | 93 | basil ganglia | 0.9999999 |
| 3 | 0 | Internal_organ_or_component | 41 | 50 | brain stem | Direction | 54 | 57 | left | 0.999811 |
| 4 | 0 | Internal_organ_or_component | 41 | 50 | brain stem | Direction | 75 | 79 | right | 0.9998203 |
| 5 | 1 | Direction | 54 | 57 | left | Internal_organ_or_component | 59 | 68 | cerebellum | 1.0 |
| 6 | 0 | Direction | 54 | 57 | left | Internal_organ_or_component | 81 | 93 | basil ganglia | 0.97616416 |
| 7 | 0 | Internal_organ_or_component | 59 | 68 | cerebellum | Direction | 75 | 79 | right | 0.953046 |
| 8 | 1 | Direction | 75 | 79 | right | Internal_organ_or_component | 81 | 93 | basil ganglia | 1.0 |
Model Information
| Model Name: | re_bodypart_directions |
| Type: | re |
| Compatibility: | Spark NLP 2.7.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [embeddings, pos_tags, train_ner_chunks, dependencies] |
| Output Labels: | [relations] |
| Language: | en |
| Dependencies: | embeddings_clinical |
Data Source
Trained on data gathered and manually annotated by John Snow Labs
Benchmarking
| relation | recall | precision | f1 |
|----------|--------|-----------|------|
| 0 | 0.87 | 0.9 | 0.88 |
| 1 | 0.99 | 0.99 | 0.99 |