The application of machine learning for identifying frailty in older patients during hospital admission.

ABSTRACT:

Background

Early identification of frail patients and early interventional treatment can minimize the frailty-related medical burden. This study investigated the use of machine learning (ML) to detect frailty in hospitalized older adults with acute illnesses.

Methods

We enrolled inpatients of the geriatric medicine ward at Taichung veterans general hospital between 2012 and 2022. We compared four ML models including logistic regression, random forest (RF), extreme gradient boosting, and support vector machine (SVM) for the prediction of frailty. The feature window as well as the prediction window was set as half a year before admission. Furthermore, Shapley additive explanation plots and partial dependence plots were used to identify Fried's frailty phenotype for interpretin

SUBMITTER: Chou YY 

PROVIDER: S-EPMC11430101 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature