Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU.

ABSTRACT:

Background

While early mobilization is commonly implemented in intensive care unit treatment guidelines to improve functional outcome, the characterization of the optimal individual dosage (frequency, level or duration) remains unclear. The aim of this study was to demonstrate that artificial intelligence-based clustering of a large ICU cohort can provide individualized mobilization recommendations that have a positive impact on the likelihood of being discharged home.

Methods

This study is an analysis of a prospective observational database of two interdisciplinary intensive care units in Munich, Germany. Dosage of mobilization is determined by sessions per day, mean duration, early mobilization as well as average and maximum level achieved. A k-means cluster analysis was c

SUBMITTER: Fuest KE 

PROVIDER: S-EPMC9808956 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature