Description
This relation extraction model links Tumor_Size extractions to their corresponding Tumor_Finding extractions.
Predicted Entities
is_size_of, O
Live Demo Open in Colab Copy S3 URI
How to use
Tumor_Finding and Tumor_Size should be included in the relation pairs.
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(["Tumor_Finding-Tumor_Size", "Tumor_Size-Tumor_Finding"])
re_model = RelationExtractionDLModel.pretrained("redl_oncology_size_biobert_wip", "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([["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 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("Tumor_Finding-Tumor_Size", "Tumor_Size-Tumor_Finding"))
val re_model = RelationExtractionDLModel.pretrained("redl_oncology_size_biobert_wip", "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("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)
import nlu
nlu.load("en.relation.oncology_size_biobert").predict("""The patient presented a 2 cm mass in her left breast, and the tumor in her other breast was 3 cm long.""")
Results
+----------+-------------+-------------+-----------+------+-------------+-------------+-----------+------+----------+
| relation| entity1|entity1_begin|entity1_end|chunk1| entity2|entity2_begin|entity2_end|chunk2|confidence|
+----------+-------------+-------------+-----------+------+-------------+-------------+-----------+------+----------+
|is_size_of| Tumor_Size| 24| 27| 2 cm|Tumor_Finding| 29| 32| mass| 0.9604708|
|is_size_of|Tumor_Finding| 62| 66| tumor| Tumor_Size| 92| 95| 3 cm|0.99731797|
+----------+-------------+-------------+-----------+------+-------------+-------------+-----------+------+----------+
Model Information
| Model Name: | redl_oncology_size_biobert_wip |
| Compatibility: | Healthcare NLP 4.2.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 401.7 MB |
References
In-house annotated oncology case reports.
Benchmarking
label recall precision f1 support
O 0.87 0.84 0.86 143.0
is_size_of 0.85 0.88 0.86 157.0
macro-avg 0.86 0.86 0.86 -