External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.

Abstract

Background and AimsUnhealthy alcohol use (UAU) is one of the leading causes of global morbidity. A machine learning approach to alcohol screening could accelerate best practices when integrated into electronic health record (EHR) systems. This study aimed to validate externally a natural language processing (NLP) classifier developed at an independent medical center.DesignRetrospective cohort study.SettingThe site for validation was a midwestern United States tertiary‐care, urban medical center that has an inpatient structured universal screening model for unhealthy substance use and an active addiction consult service.Participants/CasesUnplanned admissions of adult patients between October 23, 2017 and December 31, 2019, with EHR documentation of manual alcohol screening were included in the cohort (n = 57 605).MeasurementsThe Alcohol Use Disorders Identification Test (AUDIT) served as the reference standard. AUDIT scores ≥5 for females and ≥8 for males served as cases for UAU. To examine error in manual screening or under‐reporting, a post hoc error analysis was conducted, reviewing discordance between the NLP classifier and AUDIT‐derived reference. All clinical notes excluding the manual screening and AUDIT documentation from the EHR were included in the NLP analysis.FindingsUsing clinical notes from the first 24 hours of each encounter, the NLP classifier demonstrated an area under the receiver operating characteristic curve (AUCROC) and precision‐recall area under the curve (PRAUC) of 0.91 (95% CI = 0.89–0.92) and 0.56 (95% CI = 0.53–0.60), respectively. At the optimal cut point of 0.5, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.66 (95% CI = 0.62–0.69), 0.98 (95% CI = 0.98–0.98), 0.35 (95% CI = 0.33–0.38), and 1.0 (95% CI = 1.0–1.0), respectively.ConclusionsExternal validation of a publicly available alcohol misuse classifier demonstrates adequate sensitivity and specificity for routine clinical use as an automated screening tool for identifying at‐risk patients.