Athanasios Kolovelonis, Marina Papastergiou, Evdoxia Samara, Marios Goudas
Acute Effects of Exergaming on Students' Executive Functions and Situational Interest in Elementary Physical Education
International journal of environmental research and public health
early prediction mortality individuals hiv pwh perpetually posed formidable challenge widespread integration machine learning clinical practice researchers endeavor formulate models predicting mortality risk pwh nevertheless diverse timeframes mortality among pwh potential multitude modeling variables cast doubt efficacy current predictive model hivrelated deaths address undertook systematic review metaanalysis aiming comprehensively assess utilization machine learning early prediction hivrelated deaths furnish evidencebased support advancement artificial intelligence domain systematically combed pubmed cochrane embase web science databases november 25 2023 evaluate bias risk original studies included employed predictive model bias risk assessment tool probast metaanalysis conducted subgroup analysis based survival nonsurvival models additionally utilized metaregression explore influence death time predictive value model hivrelated deaths comprehensive review analyzed total 24 pieces literature encompassing data 401389 individuals diagnosed hiv within dataset 23 articles specifically delved deaths longterm followups outside hospital settings machine learning models applied predicting deaths comprised survival models cox regression nonsurvival models outcomes metaanalysis unveiled within training set cindex predicting deaths among people hiv pwh using predictive models stands 083 95 ci 075091 validation set cindex slightly lower 081 95 ci 078085 notably metaregression analysis demonstrated neither followup time occurrence death events significantly impacted performance machine learning models study suggests machine learning viable approach developing nontimebased predictions regarding hiv deaths nevertheless limited inclusion original studies necessitates additional multicenter studies thorough validation