Detect Assertion Status for Radiology

Description

Assign assertion status to clinical entities extracted by Radiology NER based on their context in the text.

Predicted Entities

Confirmed, Suspected, Negative.

Live Demo Open in Colab Download

How to use

Extract radiology entities using the radiology NER model in the pipeline and assign assertion status for them with assertion_dl_radiology pretrained model. Note: Example for demo purpose taken from: https://www.mtsamples.com/site/pages/sample.asp?Type=95-Radiology&Sample=1391-Chest%20PA%20&%20Lateral

...
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
  .setInputCols(["sentence", "token"])\
  .setOutputCol("embeddings")

radiology_ner = MedicalNerModel.pretrained("ner_radiology", "en", "clinical/models") \
  .setInputCols(["sentence", "token", "embeddings"]) \
  .setOutputCol("ner")

ner_converter = NerConverter() \
  .setInputCols(["sentence", "token", "ner"]) \
  .setOutputCol("ner_chunk")\
  .setWhiteList(["ImagingFindings"])

radiology_assertion = AssertionDLModel.pretrained("assertion_dl_radiology", "en", "clinical/models") \
    .setInputCols(["sentence", "ner_chunk", "embeddings"]) \
    .setOutputCol("assertion")
nlpPipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, word_embeddings, radiology_ner, ner_converter, radiology_assertion])

empty_data = spark.createDataFrame([[""]]).toDF("text")
model = LightPipeline(nlpPipeline.fit(empty_data))

text = """
INTERPRETATION: There has been interval development of a moderate left-sided pneumothorax with near complete collapse of the left upper lobe. The lower lobe appears aerated. There is stable, diffuse, bilateral interstitial thickening with no definite acute air space consolidation. The heart and pulmonary vascularity are within normal limits. Left-sided port is seen with Groshong tip at the SVC/RA junction. No evidence for acute fracture, malalignment, or dislocation."""

result = model.fullAnnotate(text)

...
val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
  .setInputCols(Array("sentence", "token"))
  .setOutputCol("embeddings")

val radiology_ner = MedicalNerModel.pretrained("ner_radiology", "en", "clinical/models")
  .setInputCols(Array("sentence", "token", "embeddings"))
  .setOutputCol("ner")

val ner_converter = NerConverter() 
  .setInputCols(Array("sentence", "token", "ner")) 
  .setOutputCol("ner_chunk")
  .setWhiteList(Array("ImagingFindings"))

val radiology_assertion = AssertionDLModel.pretrained("assertion_dl_radiology", "en", "clinical/models")
    .setInputCols(Array("sentence", "ner_chunk", "embeddings"))
    .setOutputCol("assertion")

val nlpPipeline = new Pipeline().setStages(Array(documentAssembler,  sentenceDetector, tokenizer, word_embeddings, radiology_ner, ner_converter, radiology_assertion))

text = """
INTERPRETATION: There has been interval development of a moderate left-sided pneumothorax with near complete collapse of the left upper lobe. The lower lobe appears aerated. There is stable, diffuse, bilateral interstitial thickening with no definite acute air space consolidation. The heart and pulmonary vascularity are within normal limits. Left-sided port is seen with Groshong tip at the SVC/RA junction. No evidence for acute fracture, malalignment, or dislocation."""

val data = Seq("text").toDF("text")
val result = pipeline.fit(data).transform(data)

Results

|    | ner_chunk                     | assertion   |
|---:|:------------------------------|:------------|
|  0 | pneumothorax                  | Confirmed   |
|  1 | complete collapse             | Confirmed   |
|  2 | aerated                       | Confirmed   |
|  3 | thickening                    | Confirmed   |
|  4 | acute air space consolidation | Negative    |
|  5 | within normal limits          | Confirmed   |
|  6 | acute fracture                | Negative    |
|  7 | malalignment                  | Negative    |
|  8 | dislocation                   | Negative    |

Model Information

Model Name: assertion_dl_radiology
Compatibility: Spark NLP for Healthcare 2.7.4+
License: Licensed
Edition: Official
Input Labels: [document, chunk, embeddings]
Output Labels: [assertion]
Language: en

Data Source

Custom internal labeled radiology dataset.

Benchmarking

label	 tp	 fp	 fn	 prec	 rec	 f1
Suspected	 629	 155	 159	 0.8022959	 0.7982234	 0.80025446
Negative	 417	 53	 36	 0.88723403	 0.9205298	 0.9035753
Confirmed	 2252	 173	 186	 0.9286598	 0.92370796	 0.92617726
tp: 3298 fp: 381 fn: 381 labels: 3
Macro-average	 prec: 0.87272996, rec: 0.88082033, f1: 0.8767565
Micro-average	 prec: 0.89643925, rec: 0.89643925, f1: 0.89643925