Establishment of ICU Mortality Risk Prediction Models with Machine Learning Algorithm Using MIMIC-IV Database.

ABSTRACT: Objective: The mortality rate of critically ill patients in ICUs is relatively high. In order to evaluate patients' mortality risk, different scoring systems are used to help clinicians assess prognosis in ICUs, such as the Acute Physiology and Chronic Health Evaluation III (APACHE III) and the Logistic Organ Dysfunction Score (LODS). In this research, we aimed to establish and compare multiple machine learning models with physiology subscores of APACHE III-namely, the Acute Physiology Score III (APS III)-and LODS scoring systems in order to obtain better performance for ICU mortality prediction. Methods: A total number of 67,748 patients from the Medical Information Database for Intensive Care (MIMIC-IV) were enrolled, including 7055 deceased patients, and the same number of

SUBMITTER: Pang K 

PROVIDER: S-EPMC9139972 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature