Relation Extraction between different oncological entity types (unspecific version)

Description

This relation extraction model identifies relations between dates and other clinical entities, between tumor mentions and their size, between anatomical entities and other clinical entities, and between tests and their results. In contrast to re_oncology_granular, all these relation types are labeled as is_related_to. The different types of relations can be identified considering the pairs of entities that are linked.

Predicted Entities

is_related_to, O

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How to use

Use realation pairs to include only the combinations of entities that are relevant in your case.

document_assembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer() \
    .setInputCols(["sentence"]) \
    .setOutputCol("token")

word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("embeddings")                

ner = MedicalNerModel.pretrained("ner_oncology_wip", "en", "clinical/models") \
    .setInputCols(["sentence", "token", "embeddings"]) \
    .setOutputCol("ner")

ner_converter = NerConverter() \
    .setInputCols(["sentence", "token", "ner"]) \
    .setOutputCol("ner_chunk")
        
pos_tagger = PerceptronModel.pretrained("pos_clinical", "en", "clinical/models") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("pos_tags")

dependency_parser = DependencyParserModel.pretrained("dependency_conllu", "en") \
    .setInputCols(["sentence", "pos_tags", "token"]) \
    .setOutputCol("dependencies")

re_model = RelationExtractionModel.pretrained("re_oncology", "en", "clinical/models") \
    .setInputCols(["embeddings", "pos_tags", "ner_chunk", "dependencies"]) \
    .setOutputCol("relation_extraction") \
    .setRelationPairs(["Tumor_Finding-Tumor_Size", "Tumor_Size-Tumor_Finding", "Cancer_Surgery-Relative_Date", "Relative_Date-Cancer_Surgery"]) \
    .setMaxSyntacticDistance(10)
        
pipeline = Pipeline(stages=[document_assembler,
                            sentence_detector,
                            tokenizer,
                            word_embeddings,
                            ner,
                            ner_converter,
                            pos_tagger,
                            dependency_parser,
                            re_model])

data = spark.createDataFrame([["A mastectomy was performed two months ago, and a 3 cm mass was extracted."]]).toDF("text")

result = pipeline.fit(data).transform(data)

val document_assembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")
    
val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
    .setInputCols(Array("document"))
    .setOutputCol("sentence")
    
val tokenizer = new Tokenizer()
    .setInputCols(Array("sentence"))
    .setOutputCol("token")
    
val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("embeddings")                
    
val ner = MedicalNerModel.pretrained("ner_oncology_wip", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "embeddings"))
    .setOutputCol("ner")
    
val ner_converter = new NerConverter()
    .setInputCols(Array("sentence", "token", "ner"))
    .setOutputCol("ner_chunk")

val pos_tagger = PerceptronModel.pretrained("pos_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("pos_tags")
    
val dependency_parser = DependencyParserModel.pretrained("dependency_conllu", "en")
    .setInputCols(Array("sentence", "pos_tags", "token"))
    .setOutputCol("dependencies")
    
val re_model = RelationExtractionModel.pretrained("re_oncology", "en", "clinical/models")
    .setInputCols(Array("embeddings", "pos_tags", "ner_chunk", "dependencies"))
    .setOutputCol("relation_extraction")
    .setRelationPairs(Array("Tumor_Finding-Tumor_Size", "Tumor_Size-Tumor_Finding", "Cancer_Surgery-Relative_Date", "Relative_Date-Cancer_Surgery"))
    .setMaxSyntacticDistance(10)

val pipeline = new Pipeline().setStages(Array(document_assembler,
                            sentence_detector,
                            tokenizer,
                            word_embeddings,
                            ner,
                            ner_converter,
                            pos_tagger,
                            dependency_parser,
                            re_model))

val data = Seq("A mastectomy was performed two months ago, and a 3 cm mass was extracted.").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)

Results

    chunk1        entity1         chunk2       entity2      relation confidence
mastectomy Cancer_Surgery two months ago Relative_Date is_related_to  0.9623304
      3 cm     Tumor_Size           mass Tumor_Finding is_related_to  0.7947009

Model Information

Model Name:	re_oncology
Type:	re
Compatibility:	Healthcare NLP 5.4.0+
License:	Licensed
Edition:	Official
Input Labels:	[embeddings, pos_tags, train_ner_chunks, dependencies]
Output Labels:	[relations]
Language:	en
Size:	266.3 KB

References

In-house annotated oncology case reports.

Benchmarking

     relation  recall  precision   f1
            O    0.82       0.88 0.85
is_related_to    0.89       0.83 0.86
    macro-avg    0.86       0.86 0.86

PREVIOUSRelation Extraction between Biomarkers and Results

NEXTRelation Extraction between different oncological entity types (granular version)