Relation Extraction between Tumors and Sizes

Description

This relation extraction model links Tumor_Size extractions to their corresponding Tumor_Finding extractions.

Predicted Entities

is_size_of, O

Copy S3 URI

How to use

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

data = spark.createDataFrame([["The patient presented a 2 cm mass in her left breast, and the tumor in her other breast was 3 cm long."]]).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_size", "en", "clinical/models")
    .setInputCols(Array("embeddings", "pos_tags", "ner_chunk", "dependencies"))
    .setOutputCol("relation_extraction")
    .setRelationPairs(Array("Tumor_Finding-Tumor_Size", "Tumor_Size-Tumor_Finding"))
    .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("The patient presented a 2 cm mass in her left breast, and the tumor in her other breast was 3 cm long.").toDS.toDF("text")

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

Results

| chunk1   | entity1       | chunk2   | entity2       | relation   |   confidence |
|:---------|:--------------|:---------|:--------------|:-----------|-------------:|
| 2 cm     | Tumor_Size    | mass     | Tumor_Finding | is_size_of |     0.853271 |
| tumor    | Tumor_Finding | 3 cm     | Tumor_Size    | is_size_of |     0.815623 |

Model Information

Model Name: re_oncology_size
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: 267.5 KB

References

In-house annotated oncology case reports.

Benchmarking

label         recall   precision      f1
is_size_of      0.89        0.77    0.83
O               0.75        0.88    0.81
macro-avg       0.82        0.82    0.82