Interpretable machine learning-based predictive model for malnutrition in subacute post-stroke patients: an internal and external validation study.

ABSTRACT:

Background

Malnutrition is a critical concern associated with increased mortality rates and adverse outcomes in stroke adults undergoing subacute rehabilitation. Despite its clinical significance, predictive tools for assessing malnutrition risk in this population remain limited. This study aimed to develop and validate an interpretable machine learning (ML) model to predict malnutrition risk among stroke patients during subacute rehabilitation.

Methods

This multicenter study comprised a development cohort of 802 patients from a single institution, which randomly split into training and testing sets at a 7:3 ratio. An external validation cohort of 345 patients was recruited from an independent hospital. Feature selection was conducted using the Least Absolute Shrinkage and S

SUBMITTER: Sun P 

PROVIDER: S-EPMC12824420 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature