Machine learning algorithms to predict the risk of admission to intensive care units in HIV-infected individuals: a single-centre study.

ABSTRACT: Antiretroviral therapy (ART) has transformed HIV from a rapidly progressive and fatal disease to a chronic disease with limited impact on life expectancy. However, people living with HIV(PLWHs) faced high critical illness risk due to the increased prevalence of various comorbidities and are admitted to the Intensive Care Unit(ICU). This study aimed to use machine learning to predict ICU admission risk in PLWHs. 1530 HIV patients (199 admitted to ICU) from Beijing Ditan Hospital, Capital Medical University were enrolled in the study. Classification models were built based on logistic regression(LOG), random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), artificial neural network(ANN), and extreme gradient boosting(XGB). The risk of ICU admission was predicted using the

SUBMITTER: Li J 

PROVIDER: S-EPMC12326867 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature