Detect Assertion Status (assertion_dl_en)

Description

Deep learning named entity recognition model for assertions. The SparkNLP deep learning model (NerDL) is inspired by a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM-CNN.

Assertion Status

hypothetical, present, absent, possible, conditional, associated_with_someone_else.

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How to use

Use as part of an nlp pipeline with the following stages: DocumentAssembler, SentenceDetector, Tokenizer, WordEmbeddingsModel, NerDLModel, NerConverter, AssertionDLModel.

...
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
  .setInputCols(["sentence", "token"])\
  .setOutputCol("embeddings")
clinical_ner = NerDLModel.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", "en", "clinical/models") \
    .setInputCols(["sentence", "ner_chunk", "embeddings"]) \
    .setOutputCol("assertion")
    
nlpPipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, word_embeddings, clinical_ner, ner_converter, clinical_assertion])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))

light_result = LightPipeline(model).fullAnnotate('Patient has a headache for the last 2 weeks and appears anxious when she walks fast. No alopecia noted. She denies pain')[0]

...
val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
  .setInputCols(Array("sentence", "token"))
  .setOutputCol("embeddings")
val clinical_ner = NerDLModel.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", "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 result = pipeline.fit(Seq.empty["Patient has a headache for the last 2 weeks and appears anxious when she walks fast. No alopecia noted. She denies pain"].toDS.toDF("text")).transform(data)

Results

The output is a dataframe with a sentence per row and an "assertion" column containing all of the assertion labels in the sentence. The assertion column also contains assertion character indices, and other metadata. To get only the entity chunks and assertion labels, without the metadata, select "ner_chunk.result" and "assertion.result" from your output dataframe.

|   | chunks     | entities | assertion   |
|---|------------|----------|-------------|
| 0 | a headache | PROBLEM  | present     |
| 1 | anxious    | PROBLEM  | conditional |
| 2 | alopecia   | PROBLEM  | absent      |
| 3 | pain       | PROBLEM  | absent      |

Model Information

Model Name: assertion_dl
Type: ner
Compatibility: Spark NLP 2.4.0
Edition: Official
License: Licensed
Input Labels: [sentence, ner_chunk, embeddings]
Output Labels: [assertion]
Language: [en]
Case sensitive: false

Data Source

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

Benchmarking

|    | label                        | prec | rec  | f1   |
|---:|-----------------------------:|-----:|-----:|-----:|
| 0 | absent                        | 0.94 | 0.87 | 0.91 |
| 1 | associated_with_someone_else  | 0.81 | 0.73 | 0.76 |
| 2 | conditional                   | 0.78 | 0.24 | 0.37 |
| 3 | hypothetical                  | 0.89 | 0.75 | 0.81 |
| 4 | possible                      | 0.70 | 0.52 | 0.60 |
| 5 | present                       | 0.91 | 0.97 | 0.94 |
| 6 | Macro-average                 | 0.84 | 0.68 | 0.73 |
| 7 | Micro-average                 | 0.91 | 0.91 | 0.91 |