Tyler Forbush

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.

Recognizing Questions and Answers in EMR Templates Using Natural Language Processing.

Templated boilerplate structures pose challenges to natural language processing (NLP) tools used for information extraction (IE). Routine error analyses while performing an IE task using Veterans Affairs (VA) medical records identified templates as an important cause of false positives. The baseline NLP pipeline (V3NLP) was adapted to recognize negation, questions and answers (QA) in various template types by adding a negation and slot:value identification annotator. The system was trained using a corpus of 975 documents developed as a reference standard for extracting psychosocial concepts. Iterative processing using the baseline tool and baseline+negation+QA revealed loss of numbers of concepts with a modest increase in true positives in several concept categories. Similar improvement was noted when the adapted V3NLP was used to process a random sample of 318,000 notes. We demonstrate the feasibility of adapting an NLP pipeline to recognize templates.