Description
Zero-shot Relation Extraction to extract relations between clinical entities with no training dataset, just pretrained BioBert embeddings (included in the model). This model requires Healthcare NLP 3.5.0.
Take a look at how it works in the “Open in Colab” section below.
Predicted Entities
How to use
documenter = nlp.DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = nlp.Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("tokens")
sentencer = nlp.SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentences")
words_embedder = nlp.WordEmbeddingsModel() \
.pretrained("embeddings_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens"]) \
.setOutputCol("embeddings")
ner_clinical = medical.NerModel() \
.pretrained("ner_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens", "embeddings"]) \
.setOutputCol("ner_clinical")
ner_clinical_converter = nlp.NerConverter() \
.setInputCols(["sentences", "tokens", "ner_clinical"]) \
.setOutputCol("ner_clinical_chunks")\
.setWhiteList(["PROBLEM", "TEST"]) # PROBLEM-TEST-TREATMENT
ner_posology = medical.NerModel.pretrained("ner_posology", "en", "clinical/models") \
.setInputCols(["sentences", "tokens", "embeddings"]) \
.setOutputCol("ner_posology")
ner_posology_converter = nlp.NerConverter() \
.setInputCols(["sentences", "tokens", "ner_posology"]) \
.setOutputCol("ner_posology_chunks")\
.setWhiteList(["DRUG"]) # DRUG-FREQUENCY-DOSAGE-DURATION-FORM-ROUTE-STRENGTH
chunk_merger = medical.ChunkMergeApproach()\
.setInputCols("ner_clinical_chunks", "ner_posology_chunks")\
.setOutputCol('merged_ner_chunks')
## ZERO-SHOT RE Starting...
re_model = medical.ZeroShotRelationExtractionModel.pretrained("re_zeroshot_biobert", "en", "clinical/models")\
.setInputCols(["merged_ner_chunks", "sentences"]) \
.setOutputCol("relations")\
.setMultiLabel(True)
re_model.setRelationalCategories({
"ADE": ["{DRUG} causes {PROBLEM}."],
"IMPROVE": ["{DRUG} improves {PROBLEM}.", "{DRUG} cures {PROBLEM}."],
"REVEAL": ["{TEST} reveals {PROBLEM}."]})
pipeline = nlp.Pipeline() \
.setStages([documenter,
tokenizer,
sentencer,
words_embedder,
ner_clinical,
ner_clinical_converter,
ner_posology,
ner_posology_converter,
chunk_merger,
re_model])
data = spark.createDataFrame( [["Paracetamol can alleviate headache or sickness. An MRI test can be used to find cancer."]] ).toDF("text")
results = pipeline.fit(data).transform(data)
results\
.selectExpr("explode(relations) as relation")\
.show(truncate=False)
val data = spark.createDataFrame(Seq("Paracetamol can alleviate headache or sickness. An MRI test can be used to find cancer.")).toDF("text")
val documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("tokens")
val sentencer = new SentenceDetector()
.setInputCols("document")
.setOutputCol("sentences")
val words_embedder = WordEmbeddingsModel()
.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("embeddings")
val ner_clinical = MedicalNerModel()
.pretrained("ner_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens", "embeddings"))
.setOutputCol("ner_clinical")
val ner_clinical_converter = new NerConverter()
.setInputCols(Array("sentences", "tokens", "ner_clinical"))
.setOutputCol("ner_clinical_chunks")
.setWhiteList(Array("PROBLEM", "TEST")) # PROBLEM-TEST-TREATMENT
val ner_posology = MedicalNerModel()
.pretrained("ner_posology", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens", "embeddings"))
.setOutputCol("ner_posology")
val ner_posology_converter = new NerConverter()
.setInputCols(Array("sentences", "tokens", "ner_posology"))
.setOutputCol("ner_posology_chunks")
.setWhiteList(Array("DRUG")) # DRUG-FREQUENCY-DOSAGE-DURATION-FORM-ROUTE-STRENGTH
val chunk_merger = ChunkMergeApproach()
.setInputCols("ner_clinical_chunks", "ner_posology_chunks")
.setOutputCol('merged_ner_chunks')
## ZERO-SHOT RE Starting...
val re_model = ZeroShotRelationExtractionModel.pretrained("re_zeroshot_biobert", "en", "clinical/models")
.setInputCols(Array("ner_chunks", "sentences"))
.setOutputCol("relations")
.setMultiLabel(true)
re_model.setRelationalCategories({ Map(
"CURE" -> Array("{TREATMENT} cures {PROBLEM}."),
"IMPROVE" -> Array("{TREATMENT} improves {PROBLEM}.", "{TREATMENT} cures {PROBLEM}."),
"REVEAL" -> Array("{TEST} reveals {PROBLEM}.") ))
val pipeline = new Pipeline()
.setStages(Array(documenter,
tokenizer,
sentencer,
words_embedder,
ner_clinical,
ner_clinical_converter,
ner_posology,
ner_posology_converter,
chunk_merger,
re_model))
val model = pipeline.fit(data)
val results = model.transform(data)
import nlu
nlu.load("en.relation.zeroshot_biobert").predict("""Paracetamol can alleviate headache or sickness. An MRI test can be used to find cancer.""")
Results
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|relation |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|{category, 268, 358, IMPROVE, {entity1_begin -> 0, relation -> IMPROVE, hypothesis -> Paracetamol improves sickness., confidence -> 0.98819494, nli_prediction -> entail, entity1 -> DRUG, syntactic_distance -> undefined, chunk2 -> sickness, entity2_end -> 45, entity1_end -> 10, entity2_begin -> 38, entity2 -> PROBLEM, chunk1 -> Paracetamol, sentence -> 0}, []}|
|{category, 0, 90, IMPROVE, {entity1_begin -> 0, relation -> IMPROVE, hypothesis -> Paracetamol improves headache., confidence -> 0.9929625, nli_prediction -> entail, entity1 -> DRUG, syntactic_distance -> undefined, chunk2 -> headache, entity2_end -> 33, entity1_end -> 10, entity2_begin -> 26, entity2 -> PROBLEM, chunk1 -> Paracetamol, sentence -> 0}, []} |
|{category, 536, 615, REVEAL, {entity1_begin -> 48, relation -> REVEAL, hypothesis -> An MRI test reveals cancer., confidence -> 0.9760039, nli_prediction -> entail, entity1 -> TEST, syntactic_distance -> undefined, chunk2 -> cancer, entity2_end -> 85, entity1_end -> 58, entity2_begin -> 80, entity2 -> PROBLEM, chunk1 -> An MRI test, sentence -> 1}, []} |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Model Information
| Model Name: | re_zeroshot_biobert |
| Compatibility: | Healthcare NLP 3.5.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.3 GB |
| Case sensitive: | true |
References
As it is a zero-shot relation extractor, no training dataset is necessary.