Description
Assign assertion status to clinical entities.
Predicted Entities
absent, present, conditional, associated_with_someone_else, hypothetical, possible
How to use
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_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "ner_chunk"])\
.setOutputCol("assertion_class")
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
embeddings,
ner,
ner_converter,
assertion_classifier
])
text = """Patient with severe fever and sore throat.
He shows no stomach pain and he maintained on an epidural and PCA for pain control.
He also became short of breath with climbing a flight of stairs.
After CT, lung tumor located at the right lower lobe. Father with Alzheimer.
"""
data = spark.createDataFrame([[text]]).toDF("text")
result = pipeline.fit(data).transform(data)
# show results
result.selectExpr("explode(assertion_class) as result")\
.selectExpr("result.metadata['ner_chunk'] as ner_chunk",
"result.begin as begin",
"result.begin as end",
"result.metadata['ner_label'] as ner_chunk",
"result.result as assertion").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_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "ner_chunk"])\
.setOutputCol("assertion_class")
pipeline = nlp.Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
embeddings,
ner,
ner_converter,
assertion_classifier
])
text = """Patient with severe fever and sore throat.
He shows no stomach pain and he maintained on an epidural and PCA for pain control.
He also became short of breath with climbing a flight of stairs.
After CT, lung tumor located at the right lower lobe. Father with Alzheimer.
"""
data = spark.createDataFrame([[text]]).toDF("text")
result = pipeline.fit(data).transform(data)
# show results
result.selectExpr("explode(assertion_class) as result")\
.selectExpr("result.metadata['ner_chunk'] as ner_chunk",
"result.begin as begin",
"result.begin as end",
"result.metadata['ner_label'] as ner_chunk",
"result.result as assertion").show(truncate=False)
val document_assembler = 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_clinical", "en", "clinical/models")
.setInputCols(Array("document", "ner_chunk"))
.setOutputCol("assertion_class")
val pipeline = new Pipeline().setStages(
Array(
document_assembler,
sentence_detector,
tokenizer,
embeddings,
ner,
ner_converter,
assertion_classifier
))
val text = """Patient with severe fever and sore throat.
He shows no stomach pain and he maintained on an epidural and PCA for pain control.
He also became short of breath with climbing a flight of stairs.
After CT, lung tumor located at the right lower lobe. Father with Alzheimer.
"""
val data = Seq(Array(text)).toDF("text")
val result = pipeline.fit(data).transform(data)
Results
| ner_chunk | begin | end | ner_chunk | assertion |
|:----------------|--------:|------:|:------------|:-----------------------------|
| severe fever | 13 | 13 | PROBLEM | present |
| sore throat | 30 | 30 | PROBLEM | present |
| stomach pain | 55 | 55 | PROBLEM | absent |
| pain control | 113 | 113 | PROBLEM | hypothetical |
| short of breath | 142 | 142 | PROBLEM | conditional |
| lung tumor | 202 | 202 | PROBLEM | present |
| Alzheimer | 258 | 258 | PROBLEM | associated_with_someone_else |
Model Information
| Model Name: | assertion_bert_classification_clinical |
| Compatibility: | Healthcare NLP 5.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [assertion_class] |
| Language: | en |
| Size: | 406.2 MB |
| Case sensitive: | true |
Benchmarking
label precision recall f1-score support
absent 0.964 0.976 0.970 2594
associated_with_someone_else 0.932 0.840 0.884 131
conditional 0.691 0.514 0.589 148
hypothetical 0.931 0.912 0.922 445
possible 0.814 0.699 0.752 652
present 0.963 0.976 0.969 8622
accuracy - - 0.953 12592
macro-avg 0.883 0.820 0.848 12592
weighted-avg 0.951 0.953 0.951 12592