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Interpretable machine learning prediction of in-hospital mortality in people with HIV: A cohort study in southeastern
1Department of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Background:
Hospitalized individuals with human immunodeficiency virus (HIV) remain at high risk of in-hospital mortality despite advances in combination antiretroviral therapy. Accurate and interpretable prediction tools are needed to support timely risk stratification and clinical decision-making.
Methods:
We conducted a retrospective cohort study of 2015 adults with HIV admitted to the AIDS ward of a tertiary hospital in southeastern China between 2010 and 2021. Clinical and laboratory data at admission were used to develop machine learning models predicting in-hospital mortality. Eleven algorithms were compared using area under the receiver operating characteristic curve (AUC), precision-recall curve, and decision curve analysis. Model interpretability was achieved using Shapley Additive exPlanations (SHAP). To enhance usability, we reduced predictors through SHAP-guided selection and deployed the final model as an interactive web application.
Results:
Among 2015 patients, 293 (14.54%) died during hospitalization. The Light Gradient Boosting Machine model demonstrated the best performance (AUC = 0.9008). A simplified model incorporating five admission variables, cancer antigen 125, direct bilirubin, white blood cell count, C-reactive protein, and age, achieved comparable accuracy (AUC = 0.9008). SHAP analysis provided transparent explanations at both the population and individual levels. The final model was implemented as a user-friendly online tool for individualized risk estimation.
Conclusions:
We developed and validated an interpretable machine learning model that accurately predicts in-hospital mortality among patients with HIV using five readily available clinical features. This approach enhances transparency, supports clinical decision-making, and may improve early identification of high-risk patients.