Relation Extraction between dates and other entities (ReDL)

Description

This relation extraction model links Date and Relative_Date extractions to clinical entities such as Test or Cancer_Dx.

Predicted Entities

is_date_of, O

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How to use

Each relevant relation pair in the pipeline should include one date entity (Date or Relative_Date) and a clinical entity (such as Pathology_Test, Cancer_Dx or Chemotherapy).


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 = NerConverterInternal() \
    .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_ner_chunk_filter = RENerChunksFilter()\
    .setInputCols(["ner_chunk", "dependencies"])\
    .setOutputCol("re_ner_chunk")\
    .setMaxSyntacticDistance(10)\
    .setRelationPairs(["Cancer_Dx-Date", "Date-Cancer_Dx", "Relative_Date-Cancer_Dx", "Cancer_Dx-Relative_Date", "Cancer_Surgery-Date", "Date-Cancer_Surgery", "Cancer_Surgery-Relative_Date", "Relative_Date-Cancer_Surgery"])

re_model = RelationExtractionDLModel.pretrained("redl_oncology_temporal_biobert", "en", "clinical/models")\
    .setInputCols(["re_ner_chunk", "sentence"])\
    .setOutputCol("relation_extraction")
        
pipeline = Pipeline(stages=[document_assembler,
                            sentence_detector,
                            tokenizer,
                            word_embeddings,
                            ner,
                            ner_converter,
                            pos_tagger,
                            dependency_parser,
                            re_ner_chunk_filter,
                            re_model])

data = spark.createDataFrame([["Her breast cancer was diagnosed last year."]]).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("document")
    .setOutputCol("sentence")
    
val tokenizer = new Tokenizer()
    .setInputCols("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 NerConverterInternal()
    .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_ner_chunk_filter = new RENerChunksFilter()
     .setInputCols(Array("ner_chunk", "dependencies"))
     .setOutputCol("re_ner_chunk")
     .setMaxSyntacticDistance(10)
     .setRelationPairs(Array("Cancer_Dx-Date", "Date-Cancer_Dx", "Relative_Date-Cancer_Dx", "Cancer_Dx-Relative_Date", "Cancer_Surgery-Date", "Date-Cancer_Surgery", "Cancer_Surgery-Relative_Date", "Relative_Date-Cancer_Surgery"))

val re_model = RelationExtractionDLModel.pretrained("redl_oncology_temporal_biobert", "en", "clinical/models")
      .setPredictionThreshold(0.5f)
      .setInputCols(Array("re_ner_chunk", "sentence"))
      .setOutputCol("relation_extraction")

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

val data = Seq("Her breast cancer was diagnosed last year.").toDS.toDF("text")

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

Results


+----------+---------+-------------+-----------+-------------+-------------+-------------+-----------+---------+----------+
|  relation|  entity1|entity1_begin|entity1_end|       chunk1|      entity2|entity2_begin|entity2_end|   chunk2|confidence|
+----------+---------+-------------+-----------+-------------+-------------+-------------+-----------+---------+----------+
|is_date_of|Cancer_Dx|            4|         16|breast cancer|Relative_Date|           32|         40|last year| 0.9999256|
+----------+---------+-------------+-----------+-------------+-------------+-------------+-----------+---------+----------+
 

Model Information

Model Name: redl_oncology_temporal_biobert
Compatibility: Healthcare NLP 5.4.0+
License: Licensed
Edition: Official
Input Labels: [re_ner_chunk, sentence]
Output Labels: [relation_extraction]
Language: en
Size: 405.4 MB

References

In-house annotated oncology case reports.

Benchmarking

     label  recall  precision   f1  support
         O    0.77       0.81 0.79    302.0
is_date_of    0.82       0.78 0.80    298.0
 macro-avg    0.79       0.79 0.79      -