Detect Assertion Status (assertion_dl_scope_L10R10)

Description

This model considers 10 tokens on the left and 10 tokens on the right side of the clinical entities extracted by NER models and assigns their assertion status based on their context in this scope.

Predicted Entities

present, absent, possible, conditional, associated_with_someone_else, hypothetical

Open in Colab Download Copy S3 URI

How to use

document = DocumentAssembler()\
   .setInputCol("text")\
   .setOutputCol("document")
   
sentenceDetector = SentenceDetector()\
  .setInputCols(["document"])\
  .setOutputCol("sentence")
  
token = Tokenizer()\
  .setInputCols(['sentence'])\
  .setOutputCol('token')
  
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
  .setInputCols(["sentence", "token"])\
  .setOutputCol("embeddings")
  
clinical_ner = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models") \
  .setInputCols(["sentence", "token", "embeddings"]) \
  .setOutputCol("ner")
  
ner_converter = NerConverter() \
  .setInputCols(["sentence", "token", "ner"]) \
  .setOutputCol("ner_chunk")
  
clinical_assertion = AssertionDLModel.pretrained("assertion_dl_scope_L10R10", "en", "clinical/models") \
  .setInputCols(["sentence", "ner_chunk", "embeddings"]) \
  .setOutputCol("assertion")
  
nlpPipeline = Pipeline(stages=[document,sentenceDetector, token, word_embeddings,clinical_ner,ner_converter,  clinical_assertion])


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 = nlpPipeline.fit(data).transform(data)

val documentAssembler = new DocumentAssembler() 
    .setInputCol("text") 
    .setOutputCol("document")
    
val sentenceDetector = new SentenceDetector()
  .setInputCols(Array("document"))
  .setOutputCol("sentence")
  
val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")
  
val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
  .setInputCols(Array("sentence", "token"))
  .setOutputCol("embeddings")
  
val clinical_ner = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")
  .setInputCols(Array("sentence", "token", "embeddings")) 
  .setOutputCol("ner")
  
val ner_converter = NerConverter()
  .setInputCols(Array("sentence", "token", "ner"))
  .setOutputCol("ner_chunk")
  
val clinical_assertion = AssertionDLModel.pretrained("assertion_dl_scope_L10R10", "en", "clinical/models")
  .setInputCols(Array("sentence", "ner_chunk", "embeddings"))
  .setOutputCol("assertion")
  
val pipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDetector, tokenizer, word_embeddings, clinical_ner, ner_converter, clinical_assertion))
val data = Seq("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.").toDF("text")


val result = pipeline.fit(data).transform(data)

import nlu
nlu.load("en.assert.l10r10").predict("""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.""")

Results

+---------------+---------+----------------------------+
|chunk          |entity   |assertion                   |
+---------------+---------+----------------------------+
|severe fever   |PROBLEM  |present                     |
|sore throat    |PROBLEM  |present                     |
|stomach pain   |PROBLEM  |absent                      |
|an epidural    |TREATMENT|present                     |
|PCA            |TREATMENT|present                     |
|pain control   |PROBLEM  |present                     |
|short of breath|PROBLEM  |conditional                 |
|CT             |TEST     |present                     |
|lung tumor     |PROBLEM  |present                     |
|Alzheimer      |PROBLEM  |associated_with_someone_else|
+---------------+---------+----------------------------+

Model Information

Model Name:	assertion_dl_scope_L10R10
Compatibility:	Healthcare NLP 3.4.2+
License:	Licensed
Edition:	Official
Input Labels:	[document, chunk, embeddings]
Output Labels:	[assertion]
Language:	en
Size:	1.4 MB

References

Trained on augmented version of 2010 i2b2/VA dataset on concepts, assertions, and relations in clinical text with ‘embeddings_clinical’. https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/

Benchmarking

label                         tp    fp   fn   prec        rec        f1        
absent                        812   48   71   0.94418603  0.9195923  0.93172693
present                       2463  127  141  0.9509652   0.9458525  0.948402  
conditional                   25    19   28   0.5681818   0.4716981  0.5154639 
associated_with_someone_else  36    7    9    0.8372093   0.8        0.8181818 
hypothetical                  147   31   28   0.8258427   0.84       0.8328612 
possible                      159   87   42   0.64634144  0.7910448  0.71140933
Macro-average	              -     -    -    0.79545444  0.7946979  0.795076  
Micro-average	              -     -    -    0.91946477  0.9194648  0.91946477

PREVIOUSPipeline to Detect Clinical Entities

NEXTDetect Assertion Status (assertion_dl_scope_L10R10)