Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data.

• Tác giả: Anne N Banducci, Daniel Chen, Michael Davenport, Jennifer R Fonda, Zig Hinds, Nicholas A Livingston, Lauren B Loeffel, Amar D Mandavia, Brittany Mathes-Winnicki, Frank Meng, Noam Newberger, Clara E Roth, Alexis Sarpong, Rebecca Sistad Hall, Maria Ting
• Ngôn ngữ: eng
• Ký hiệu phân loại: 809.008 History and description with respect to kinds of persons
• Thông tin xuất bản: United States : Journal of psychopathology and clinical science , 2025
• Bộ sưu tập: NCBI
• ID: 725931

Tóm tắt

Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (