Description
This is a BERT-based model for classification of clinical documents sections. This model is trained on clinical document sections without the section header in the text, e.g., when splitting the document with ChunkSentenceSplitter annotator with parameter setInsertChunk=False.
Predicted Entities
Consultation and Referral, Habits, Complications and Risk Factors, Diagnostic and Laboratory Data, Discharge Information, History, Impression, Patient Information, Procedures, Other
How to use
document_assembler = nlp.DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = nlp.Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
sequenceClassifier = medical.BertForSequenceClassification.pretrained("bert_sequence_classifier_clinical_sections_headless", "en", "clinical/models")\
.setInputCols(["document", "token"])\
.setOutputCol("prediction")\
.setCaseSensitive(False)
pipeline = nlp.Pipeline(stages=[
document_assembler,
tokenizer,
sequenceClassifier
])
example_df = spark.createDataFrame(
[["""It was a pleasure taking care of you! You came to us with
stomach pain and worsening distension. While you were here we
did a paracentesis to remove 1.5L of fluid from your belly. We
also placed you on you 40 mg of Lasix and 50 mg of Aldactone to
help you urinate the excess fluid still in your belly. As we
discussed, everyone has a different dose of lasix required to
make them urinate and it's likely that you weren't taking a high
enough dose. Please take these medications daily to keep excess
fluid off and eat a low salt diet. You will follow up with Dr.
___ in liver clinic and from there have your colonoscopy
and EGD scheduled. """]]).toDF("text")
result = pipeline.fit(example_df).transform(example_df)
result.select("prediction.result").show(truncate=False)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained()
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val seq = BertForSequenceClassification.pretrained("bert_sequence_classifier_clinical_sections_headless", "en", "clinical/models")
.setInputCols(Array("token", "sentence"))
.setOutputCol("label")
.setCaseSensitive(false)
val pipeline = new Pipeline().setStages(Array(
documentAssembler,
sentenceDetector,
tokenizer,
seq))
val test_sentences = """It was a pleasure taking care of you! You came to us with
stomach pain and worsening distension. While you were here we
did a paracentesis to remove 1.5L of fluid from your belly. We
also placed you on you 40 mg of Lasix and 50 mg of Aldactone to
help you urinate the excess fluid still in your belly. As we
discussed, everyone has a different dose of lasix required to
make them urinate and it's likely that you weren't taking a high
enough dose. Please take these medications daily to keep excess
fluid off and eat a low salt diet. You will follow up with Dr.
___ in liver clinic and from there have your colonoscopy
and EGD scheduled. """"
val example = Seq(test_sentences).toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("en.classify.bert_sequence.clinical_sections_headless").predict("""It was a pleasure taking care of you! You came to us with
stomach pain and worsening distension. While you were here we
did a paracentesis to remove 1.5L of fluid from your belly. We
also placed you on you 40 mg of Lasix and 50 mg of Aldactone to
help you urinate the excess fluid still in your belly. As we
discussed, everyone has a different dose of lasix required to
make them urinate and it's likely that you weren't taking a high
enough dose. Please take these medications daily to keep excess
fluid off and eat a low salt diet. You will follow up with Dr.
___ in liver clinic and from there have your colonoscopy
and EGD scheduled. """)
Results
+-----------------------+
|result |
+-----------------------+
|[Discharge Information]|
+-----------------------+
Model Information
| Model Name: | bert_sequence_classifier_clinical_sections_headless |
| Compatibility: | Healthcare NLP 5.1.4+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 406.6 MB |
| Case sensitive: | false |
| Max sentence length: | 512 |
References
In-house annotation of clinical documents.
Sample text from the training dataset
It was a pleasure taking care of you! You came to us with stomach pain and worsening distension. While you were here we did a paracentesis to remove 1.5L of fluid from your belly. We also placed you on you 40 mg of Lasix and 50 mg of Aldactone to help you urinate the excess fluid still in your belly. As we discussed, everyone has a different dose of lasix required to make them urinate and it’s likely that you weren’t taking a high enough dose. Please take these medications daily to keep excess fluid off and eat a low salt diet. You will follow up with Dr. ___ in liver clinic and from there have your colonoscopy and EGD scheduled.
Benchmarking
label precision recall f1-score support
Consultation_and_Referral 0.655949 0.890830 0.755556 229
Other 0.954545 0.933333 0.943820 45
Habits 0.872727 0.800000 0.834783 60
Complications_and_Risk_Factors 0.997468 0.989950 0.993695 398
Diagnostic_and_Laboratory_Data 0.887417 0.676768 0.767908 396
Discharge_Information 0.792000 0.763496 0.777487 389
History 0.873810 0.910670 0.891859 403
Impression 0.843537 0.909535 0.875294 409
Patient_Information 0.804569 0.786600 0.795483 403
Procedures 0.875912 0.865385 0.870617 416