Across six models and 2,200 HIV outpatients, including demographic features always improved multi-label comorbidity prediction, with XGBoost best at 45.8% macro F1; gender was recoverable from labs at 92.8%.
Chronic kidney disease stage identification in hiv infected patients using machine learning
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Proactive HIV Care: AI-Based Comorbidity Prediction from Routine EHR Data
Across six models and 2,200 HIV outpatients, including demographic features always improved multi-label comorbidity prediction, with XGBoost best at 45.8% macro F1; gender was recoverable from labs at 92.8%.