Updating mortality risk estimation in intensive care units from high-dimensional electronic health records with incomplete data.

ABSTRACT:

Background

The risk of mortality in intensive care units (ICUs) is currently addressed by the implementation of scores using admission data. Their performances are satisfactory when complications occur early after admission; however, they may become irrelevant in the case of long hospital stays. In this study, we developed predictive models of short-term mortality in the ICU from longitudinal data.

Methods

Using data collected throughout patients' stays of at least 48 h from the MIMIC-III database, several statistical learning approaches were compared, including deep neural networks and penalized regression. Missing data were handled using complete-case analysis or multiple imputation.

Results

Complete-case analyses from 19 predictors showed good discrimination (AUC >

SUBMITTER: Bouvarel B 

PROVIDER: S-EPMC10466694 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature