Description
Assign assertion status to clinical entities.
Predicted Entities
Present, Past, Family, Absent, Hypothetical, Possible
How to use
# Test classifier in Spark NLP pipeline
document_assembler = DocumentAssembler()\
.setInputCol("text") \
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols("document") .setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["document"])\
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)
ner = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverterInternal()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")\
.setWhiteList(["PROBLEM"])
assertion_classifier = BertForAssertionClassification.pretrained("assertion_bert_classification_oncology", "en", "clinical/models")\
.setInputCols(["sentence", "ner_chunk"])\
.setOutputCol("assertion_class")\
.setCaseSensitive(False)
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
embeddings,
ner,
ner_converter,
assertion_classifier
])
# Generating example
data = spark.createDataFrame(["he was begun on physical therapy but remained agitated .",
"there were no meatal blood ."], StringType()).toDF("text")
result = pipeline.fit(data).transform(data)
# Checking results
result.select("text", "assertion_class.result").show(truncate=False)
# Test classifier in Spark NLP pipeline
document_assembler = nlp.DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
sentence_detector = nlp.SentenceDetector()\
.setInputCols("document")\
.setOutputCol("sentence")
tokenizer = nlp.Tokenizer() \
.setInputCols(["sentence"]) \
.setOutputCol("token")
embeddings = nlp.WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)
ner = medical.NerModel.pretrained("ner_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = medical.NerConverterInternal()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")\
.setWhiteList(["PROBLEM"])
assertion_classifier = medical.BertForAssertionClassification.pretrained("assertion_bert_classification_oncology", "en", "clinical/models")\
.setInputCols(["sentence", "ner_chunk"])\
.setOutputCol("assertion_class")\
.setCaseSensitive(False)
pipeline = nlp.Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
embeddings,
ner,
ner_converter,
assertion_classifier
])
# Generating example
data = spark.createDataFrame(["he was begun on physical therapy but remained agitated .",
"there were no meatal blood ."], StringType()).toDF("text")
result = pipeline.fit(data).transform(data)
val documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentences")
.setOutputCol("token")
val embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
.setCaseSensitive(False)
val ner = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
.setWhiteList(Array("PROBLEM"))
val assertion_classifier = BertForAssertionClassification.pretrained("assertion_bert_classification_oncology", "en", "clinical/models")
.setInputCols(Array("document", "ner_chunk"))
.setOutputCol("assertion_class")
.setCaseSensitive(False)
val pipeline = new Pipeline().setStages(
Array(
documenter,
sentence_detector,
tokenizer,
embeddings,
ner,
ner_converter,
assertion_classifier
))
val data = Seq(Array("he was begun on physical therapy but remained agitated .",
"there were no meatal blood .")).toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+----------------------------------------------------------------+-------+
|text |result |
+----------------------------------------------------------------+-------+
|he was begun on physical therapy but remained agitated . |present|
|there were no meatal blood . |absent |
+----------------------------------------------------------------+-------+
Model Information
| Model Name: | assertion_bert_classification_oncology |
| Compatibility: | Healthcare NLP 5.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [assertion_class] |
| Language: | en |
| Size: | 406.3 MB |
| Case sensitive: | true |