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An Automated Feature Engineering for Digital Rectal Examination Documentation using Natural Language Processing. AMIA ... Annual Symposium proceedings. AMIA Symposium Bozkurt, S., Park, J. I., Kan, K. M., Ferrari, M., Rubin, D. L., Brooks, J. D., Hernandez-Boussard, T. 2018; 2018: 288–94

Abstract

Digital rectal examination (DRE) is considered a quality metric for prostate cancer care. However, much of the DRE related rich information is documented as free-text in clinical narratives. Therefore, we aimed to develop a natural language processing (NLP) pipeline for automatic documentation of DRE in clinical notes using a domain-specific dictionary created by clinical experts and an extended version of the same dictionary learned by clinical notes using distributional semantics algorithms. The proposed pipeline was compared to a baseline NLP algorithm and the results of the proposed pipeline were found superior in terms of precision (0.95) and recall (0.90) for documentation of DRE. We believe the rule-based NLP pipeline enriched with terms learned from the whole corpus can provide accurate and efficient identification of this quality metric.

View details for PubMedID 30815067