Classifying the Indication for Colonoscopy Procedures: A Comparison of NLP Approaches in a Diverse National Healthcare System.
In order to measure the level of utilization of colonoscopy procedures, identifying the primary indication for the procedure is required. Colonoscopies may be utilized not only for screening, but also for diagnostic or therapeutic purposes. To determine whether a colonoscopy was performed for screening, we created a natural language processing system to identify colonoscopy reports in the electronic medical record system and extract indications for the procedure. A rule-based model and three machine-learning models were created using 2,000 manually annotated clinical notes of patients cared for in the Department of Veterans Affairs. Performance of the models was measured and compared. Analysis of the models on a test set of 1,000 documents indicates that the rule-based system performance stays fairly constant as evaluated on training and testing sets. However, the machine learning model without feature selection showed significant decrease in performance. Therefore, rule-based classification system appears to be more robust than a machine-learning system in cases when no feature selection is performed.
VA Salt Lake City Health Care System.
Olga V
OV
Patterson
VA Salt Lake City Health Care System.
Tyler B
TB
Forbush
VA Ann Arbor HSR&D Center for Clinical Management Research.
Sameer D
SD
Saini
University of Michigan Medical School, Ann Arbor, MI.
Stephanie E
SE
Moser
VA Salt Lake City Health Care System.
Scott L
SL
DuVall
D003108:Colonic Diseases / Q000175:diagnosis* / Q000601:surgery; D003113:Colonoscopy / Q000706:statistics & numerical data*; D057225:Data Mining / Q000379:methods; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D057286:Electronic Health Records / Q000145:classification*; D006787:Hospitals, Veterans / Q000706:statistics & numerical data; D006801:Humans; D000069550:Machine Learning; D008403:Mass Screening / Q000379:methods; D000069342:Medical Overuse / Q000517:prevention & control*; D009313:National Health Programs / Q000706:statistics & numerical data; D009323:Natural Language Processing; D020380:Needs Assessment / Q000458:organization & administration; D014481:United States