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
In the table below, re_bodypart_directions RE model, its labels, optimal NER model, and meaningful relation pairs are illustrated.
| RE MODEL | RE MODEL LABELS | NER MODEL | RE PAIRS |
|---|---|---|---|
| re_bodypart_directions | 0,1 | ner_jsl | [“direction-external_body_part_or_region”, “external_body_part_or_region-direction”, “direction-internal_organ_or_component”, “internal_organ_or_component-direction”] |
documenter = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentencer = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentences")
tokenizer = Tokenizer()\
.setInputCols(["sentences"])\
.setOutputCol("tokens")
words_embedder = WordEmbeddingsModel()\
.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentences", "tokens"])\
.setOutputCol("embeddings")
pos_tagger = PerceptronModel()\
.pretrained("pos_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens"])\
.setOutputCol("pos_tags")
ner_tagger = MedicalNerModel()\
.pretrained("jsl_ner_wip_greedy_clinical","en","clinical/models")\
.setInputCols("sentences", "tokens", "embeddings")\
.setOutputCol("ner_tags")
ner_chunker = NerConverterInternal()\
.setInputCols(["sentences", "tokens", "ner_tags"])\
.setOutputCol("ner_chunks")
dependency_parser = DependencyParserModel()\
.pretrained("dependency_conllu", "en")\
.setInputCols(["sentences", "pos_tags", "tokens"])\
.setOutputCol("dependencies")
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 documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentencer = new SentenceDetector()
.setInputCols("document")
.setOutputCol("sentences")
val tokenizer = new Tokenizer()
.setInputCols("sentences")
.setOutputCol("tokens")
val words_embedder = WordEmbeddingsModel()
.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("embeddings")
val pos_tagger = PerceptronModel()
.pretrained("pos_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("pos_tags")
val ner_tagger = sparknlp.annotators.NerDLModel()
.pretrained("jsl_ner_wip_greedy_clinical","en","clinical/models")
.setInputCols("sentences", "tokens", "embeddings")
.setOutputCol("ner_tags")
val ner_chunker = new NerConverterInternal()
.setInputCols(Array("sentences", "tokens", "ner_tags"))
.setOutputCol("ner_chunks")
val dependency_parser = DependencyParserModel()
.pretrained("dependency_conllu", "en")
.setInputCols(Array("sentences", "pos_tags", "tokens"))
.setOutputCol("dependencies")
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 text = """ MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia """
val data = Seq(text).toDS.toDF("text")
val results = pipeline.fit(data).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
label recall precision f1
0 0.87 0.9 0.88
1 0.99 0.99 0.99