Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.

ABSTRACT:

Background

The accuracy of current tools for predicting adverse events in older inpatients with possible sarcopenia is still insufficient to develop individualized nutrition-related management strategies. The objectives were to develop a machine learning model based on nutritional assessment for the prediction of all-cause death and infectious complications.

Methods

A cohort of older patients with possible sarcopenia (divided into training group [70%] and validation group [30%]) from 30 hospitals in 14 major cities in China was retrospectively analyzed. Clinical characteristics, laboratory examination, Nutritional risk Screening-2002 (NRS-2002) and mini-nutritional Assessment-Short form (MNA-SF) were used to construct machine learning models to predict in-hospital adverse ev

SUBMITTER: Liu C 

PROVIDER: S-EPMC11846711 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature