Prediction algorithm for ICU mortality and length of stay using machine learning.

ABSTRACT: Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length of stay in intensive care unit (ICU) patients using machine learning, and to identify the variables contributing to the precise prediction or classification of patients. Patients (n = 12,747) admitted to the ICU at Chiba University Hospital were randomly assigned to the training and test cohorts. After learning using the variables on admission in the training cohort, the area under the curve (AUC) was analyzed in the test cohort to evaluate the predictive accuracy of the supervised machine learning classifiers, including random forest (RF) for

SUBMITTER: Iwase S 

PROVIDER: S-EPMC9334583 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature