Description
This model extracts relations between posology-related terminology.
Predicted Entities
DRUG-DOSAGE, DRUG-FREQUENCY, DRUG-ADE, DRUG-FORM, DRUG-ROUTE, DRUG-DURATION, DRUG-STRENGTH
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector() \
.setInputCols(["document"]) \
.setOutputCol("sentences")
tokenizer = Tokenizer() \
.setInputCols(["sentences"]) \
.setOutputCol("tokens")
words_embedder = WordEmbeddingsModel()\
.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentences", "tokens"])\
.setOutputCol("embeddings")
pos_tagger = PerceptronModel()\
.pretrained("pos_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens"])\
.setOutputCol("pos_tags")
ner_tagger = MedicalNerModel()\
.pretrained("ner_posology", "en", "clinical/models")\
.setInputCols("sentences", "tokens", "embeddings")\
.setOutputCol("ner_tags")
ner_chunker = NerConverterInternal()\
.setInputCols(["sentences", "tokens", "ner_tags"])\
.setOutputCol("ner_chunks")
dependency_parser = DependencyParserModel()\
.pretrained("dependency_conllu", "en")\
.setInputCols(["sentences", "pos_tags", "tokens"])\
.setOutputCol("dependencies")
reModel = RelationExtractionModel()\
.pretrained("posology_re")\
.setInputCols(["embeddings", "pos_tags", "ner_chunks", "dependencies"])\
.setOutputCol("relations")\
.setMaxSyntacticDistance(4)
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
words_embedder,
pos_tagger,
ner_tagger,
ner_chunker,
dependency_parser,
reModel])
empty_data = spark.createDataFrame([[""]]).toDF("text")
model = pipeline.fit(empty_data)
light_pipeline = LightPipeline(model)
result = light_pipeline.fullAnnotate("The patient was prescribed 1 unit of Advil for 5 days after meals. The patient was also given 1 unit of Metformin daily. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.")
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
sentence_detector = SentenceDetector()
.setInputCols("document")
.setOutputCol("sentences")
tokenizer = Tokenizer()
.setInputCols("sentences")
.setOutputCol("tokens")
val words_embedder = WordEmbeddingsModel()
.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("embeddings")
val pos_tagger = PerceptronModel()
.pretrained("pos_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("pos_tags")
val ner_tagger = MedicalNerModel()
.pretrained("ner_posology", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens", "embeddings"))
.setOutputCol("ner_tags")
val ner_chunker = new NerConverterInternal()
.setInputCols(Array("sentences", "tokens", "ner_tags"))
.setOutputCol("ner_chunks")
val dependency_parser = DependencyParserModel()
.pretrained("dependency_conllu", "en")
.setInputCols(Array("sentences", "pos_tags", "tokens"))
.setOutputCol("dependencies")
val re_Model = RelationExtractionModel()
.pretrained("posology_re")
.setInputCols(Array("embeddings", "pos_tags", "ner_chunks", "dependencies"))
.setOutputCol("relations")
.setMaxSyntacticDistance(4)
val pipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
words_embedder,
pos_tagger,
ner_tagger,
ner_chunker,
dependecy_parser,
re_Model))
val data = Seq("The patient was prescribed 1 unit of Advil for 5 days after meals. The patient was also given 1 unit of Metformin daily. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.").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 |
|----------------|----------|---------------|-------------|------------------|-----------|---------------|-------------|------------------|------------|
| DURATION-DRUG | DURATION | 493 | 500 | five-day | DRUG | 512 | 522 | amoxicillin | 1.0 |
| DRUG-DURATION | DRUG | 681 | 693 | dapagliflozin | DURATION | 695 | 708 | for six months | 1.0 |
| DRUG-ROUTE | DRUG | 1940 | 1946 | insulin | ROUTE | 1948 | 1951 | drip | 1.0 |
| DOSAGE-DRUG | DOSAGE | 2255 | 2262 | 40 units | DRUG | 2267 | 2282 | insulin glargine | 1.0 |
| DRUG-FREQUENCY | DRUG | 2267 | 2282 | insulin glargine | FREQUENCY | 2284 | 2291 | at night | 1.0 |
| DOSAGE-DRUG | DOSAGE | 2295 | 2302 | 12 units | DRUG | 2307 | 2320 | insulin lispro | 1.0 |
| DRUG-FREQUENCY | DRUG | 2307 | 2320 | insulin lispro | FREQUENCY | 2322 | 2331 | with meals | 1.0 |
| DRUG-STRENGTH | DRUG | 2339 | 2347 | metformin | STRENGTH | 2349 | 2355 | 1000 mg | 1.0 |
| DRUG-FREQUENCY | DRUG | 2339 | 2347 | metformin | FREQUENCY | 2357 | 2371 | two times a day | 1.0 |
Model Information
| Model Name: | posology_re |
| Compatibility: | Healthcare NLP 2.5.5+ |
| Edition: | Official |
| License: | Licensed |
| Language: | [en] |