{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T06:09:53Z","timestamp":1784182193362,"version":"3.55.0"},"reference-count":61,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Viral non-suppression is the primary actionable risk state in routine HIV care, yet most individuals are identified after virological failure and\/or drug resistance, rather than proactively. In Uganda and similar resource-limited settings, routine electronic medical records (EMR) are collected at scale but remain underused for targeted, data-enabled risk stratification. We aimed to develop and internally validate machine learning and regularized regression models for predicting viral non-suppression using routine monitoring data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We developed and internally validated prediction models for viral non-suppression (viral load \u22651,000 copies\/mL) using routinely recorded EMR variables from the TASO Uganda open cohort (2014\u20132024;\n                      <jats:italic>n<\/jats:italic>\n                      = 33,384). Twenty variables were used across four models: logistic regression (LR), elastic net regularized logistic regression (ENET), random forest (RF), and extreme gradient boosting (XGB), evaluated on a stratified 80:20 test set. Precision-recall AUC (PR-AUC) was the primary metric; ROC-AUC, Brier score, and decision curve analysis were assessed; bootstrap 95% CIs (2,000 replicates) were computed for discrimination metrics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      On the test set (\n                      <jats:italic>n<\/jats:italic>\n                      = 6,677), RF achieved PR-AUC 0.248 (95% CI 0.207\u20130.291) and ROC-AUC 0.758 (0.732\u20130.780); ENET achieved PR-AUC 0.237 (0.198\u20130.279) and ROC-AUC 0.750 (0.726\u20130.772); confidence intervals overlapped across all four models. RF and ENET achieved Brier scores of 0.055 (8% below the null of 0.060) and maximum net benefit of 0.055 in decision curve analysis. At the capacity-first threshold (top 5% predicted risk), RF flagged 325 individuals (4.9%; PPV 0.338, NPV 0.949) and ENET flagged 334 (5.0%; PPV 0.305, NPV 0.948). Current ART class (PI-based: OR 11.07; NNRTI-based: OR 4.80), poor adherence (OR 7.71), TB history (OR 2.08), and male sex (OR 1.62) were the strongest predictors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Routine EMR data support meaningful, calibrated viral non-suppression risk prediction across a large, multi-site Ugandan HIV program. At a capacity-first threshold, both models achieved approximately five-fold enrichment over background prevalence, with clinical utility confirmed by decision curve analysis. Prospective external validation and workflow integration are required before deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1869992","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:36:20Z","timestamp":1784180180000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting HIV viral non-suppression in Uganda: development and validation of machine learning and risk stratification models using routine EMR data"],"prefix":"10.3389","volume":"9","author":[{"given":"Maria Magdalene","family":"Namaganda","sequence":"first","affiliation":[{"name":"Department of Immunology and Molecular Biology, School of Biomedical Sciences, College of Health Sciences, Makerere University","place":["Kampala, Uganda"]},{"name":"Institute for Global Health Sciences, University of California, San Francisco","place":["San Francisco, CA, United 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