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
Live Demo Open in Colab Copy S3 URI
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 = 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_temporal_wip", "en", "clinical/models") \
.setInputCols(["embeddings", "pos_tags", "ner_chunk", "dependencies"]) \
.setOutputCol("relation_extraction") \
.setRelationPairs(["Cancer_Dx-Date", "Date-Cancer_Dx", "Relative_Date-Cancer_Dx", "Cancer_Dx-Relative_Date", "Cancer_Surgery-Date", "Date-Cancer_Surgery", "Cancer_Surger-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([["Her breast cancer was diagnosed three years ago, and a bilateral mastectomy was performed last month."]]).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_temporal_wip", "en", "clinical/models")
.setInputCols(Array("embeddings", "pos_tags", "ner_chunk", "dependencies"))
.setOutputCol("relation_extraction")
.setRelationPairs(Array("Cancer_Dx-Date", "Date-Cancer_Dx", "Relative_Date-Cancer_Dx", "Cancer_Dx-Relative_Date", "Cancer_Surgery-Date", "Date-Cancer_Surgery", "Cancer_Surger-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("Her breast cancer was diagnosed three years ago, and a bilateral mastectomy was performed last month.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.relation.oncology_temporal_wip").predict("""Her breast cancer was diagnosed three years ago, and a bilateral mastectomy was performed last month.""")
Results
chunk1 entity1 chunk2 entity2 relation confidence
breast cancer Cancer_Dx three years ago Relative_Date is_date_of 0.5886298
breast cancer Cancer_Dx last month Relative_Date O 0.9708738
three years ago Relative_Date mastectomy Cancer_Surgery O 0.6020852
mastectomy Cancer_Surgery last month Relative_Date is_date_of 0.9277692
Model Information
| Model Name: | re_oncology_temporal_wip |
| Type: | re |
| Compatibility: | Healthcare NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [embeddings, pos_tags, train_ner_chunks, dependencies] |
| Output Labels: | [relations] |
| Language: | en |
| Size: | 265.6 KB |
References
In-house annotated oncology case reports.
Benchmarking
label recall precision f1
O 0.79 0.76 0.77
is_date_of 0.74 0.77 0.75
macro-avg 0.76 0.76 0.76