{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T17:50:25Z","timestamp":1781977825884,"version":"3.54.5"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"National Institute on Minority Health and Health Disparities through the Texas Center for Health Disparities","award":["5S21MD012472-05"],"award-info":[{"award-number":["5S21MD012472-05"]}]},{"name":"National Institutes of Health\/Artificial Intelligence\/Machine Learning Consortium to Advance Health Equity and Researcher Diversity","award":["1OT2OD032581-01"],"award-info":[{"award-number":["1OT2OD032581-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Large administrative datasets enable investigation of rare outcomes, but accurate individual-level prediction remains challenging. Emerging evidence suggests that glucagon-like peptide-1 receptor agonists (GLP-1 RAs), used to treat diabetes and obesity, may reduce alcohol craving and intake. We examined predictors of incident alcohol-related disorder (ARD) using inverse probability\u2013weighted (IPW) machine learning models, focusing on GLP-1 RA use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective cohort study using the MarketScan Multi-State Medicaid Database (N\u2009=\u200996\u00a0460). Type 2 diabetic adults aged 18\u201364\u00a0years with GLP-1 RA prescription coverage (2022\u20132023) and no ARD in 2022 were included. Baseline variables included demographics, social risk factors (Z-codes), comorbidities, insurance characteristics, and GLP-1 RA use. Propensity scores were used to derive stabilized IPW weights to adjust for confounding in GLP-1 RA exposure. An Extreme Gradient Boosting model with IPW was trained using a 70\/30 train\u2013test split with five-fold cross-validation and probability calibration. Performance was evaluated using Area Under the Receiver Operating Characteristics Curve (AUROC), recall, and precision. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>GLP-1 RAs were used by 21.7% of individuals; 2.1% (N\u2009=\u20092004) developed incident ARD. The model demonstrated modest discrimination (AUROC\u2009=\u20090.67). Recall and precision were 0.28 and 0.05, respectively. SHAP analysis ranked GLP-1 RA use as the sixth most influential predictor and showed an association with lower predicted ARD risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Predicting rare outcomes remains difficult, particularly for identifying positive cases. Interpretable machine learning identified GLP-1 RA use as an important feature associated with lower predicted ARD risk, warranting further investigation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/alcalc\/agag045","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:45:38Z","timestamp":1780055138000},"source":"Crossref","is-referenced-by-count":0,"title":["Interpretable machine learning for rare events: an inverse probability weighted application to understanding glucagon-like peptide-1 receptor agonists use and incident alcohol-related disorder"],"prefix":"10.1093","volume":"61","author":[{"given":"Rinoj","family":"Gautam","sequence":"first","affiliation":[{"name":"College of Pharmacy, UNT Health, Fort Worth , 3500 Camp Bowie Blvd, Fort Worth, TX 76107 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5105-0951","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, JPS Health Network , 1500 S. Main St., Fort Worth, TX 76104 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Pharmacy, UNT Health, Fort Worth , 3500 Camp Bowie Blvd, Fort Worth, TX 76107 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rolake","family":"Neba","sequence":"additional","affiliation":[{"name":"College of Pharmacy, UNT Health, Fort Worth , 3500 Camp Bowie Blvd, Fort Worth, TX 76107 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Usha","family":"Sambamoorthi","sequence":"additional","affiliation":[{"name":"Health Outcomes Research and AI Innovation, Department of Pharmacy Practice, School of Pharmacy, Temple University , 1801 N. 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