Machine learning-driven in-hospital mortality prediction in HIV/AIDS patients with Cytomegalovirus infection: a single-centred retrospective study

( ) is a widely disseminated betaherpesvirus that typically induces latant infections. In immunocompromised populations, especially transplant and HIV-infected patients, infection increases in-hospital mortality. Although machine learning models have been widely used in clinical diagnosis and prognosis prediction, reports on machine learning model predictions for the in-hospital mortality of HIV/AIDS patients with infection have not been reported. Analyze the general gemographic and clinical characteristics of HIV/AIDS patients with infection and identify the factors affecting the prognosis of this population, which will help to reduce their in-hospital mortality. Hospitalized HIV/AIDS patients with infection were recruited from the Fourth People's Hospital of Nanning, Guangxi, from 2012 to 2019. After dividing them into survival and death groups based on their in-hospital survival status, their general and clinical profiles were described. Following 1 : 3 propensity score matching to equalize baseline characteristics, three machine-learning models (Random Forest, Support Vector Machine and eXtreme Gradient Boosting) were deployed to forecast factors influencing prognosis. The SHapley Additive exPlanations tool explained the models. A total of 1102 HIV/AIDS patients with infection were analysed. There was no statistical difference in the general condition of the study subjects ( ＞0.05). Prevalent complications/coinfections included pneumonia (63.6%), (47.2%) and oral fungal infections (44.6%). There were significant differences between the groups in pneumonia, and hypoproteinaemia (