Machine-learning models are superior to severity scoring systems for the prediction of the mortality of critically ill patients in a tertiary medical center.

ABSTRACT:

Background

Intensive care unit (ICU) mortality prediction helps to guide therapeutic decision making for critically ill patients. Several scoring systems based on statistical techniques have been developed for this purpose. In this study, we developed a machine-learning model to predict patient mortality in the very early stage of ICU admission.

Methods

This study was performed with data from all patients admitted to the intensive care units of a tertiary medical center in Taiwan from 2009 to 2018. The patients' comorbidities, co-medications, vital signs, and laboratory data on the day of ICU admission were obtained from electronic medical records. We constructed random forest and extreme gradient boosting (XGBoost) models to predict ICU mortality, and compared their perform

SUBMITTER: Chou RH 

PROVIDER: S-EPMC12718789 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature