Predictive Models for Early Infection Detection in Nursing Home Residents: Evaluation of Imputation Techniques and Complementary Data Sources.

ABSTRACT: Background: Aging in Western societies poses a growing challenge, placing increasing pressure on healthcare costs. Early identification of infections in elderly nursing home residents is crucial to reduce complications, mortality, and the burden on emergency departments. Methods: We performed a comparative analysis of machine learning models using XGBoost classifiers for infection detection, addressing incomplete daily physiological measurements (Heart Rate, Oxygen Saturation, Body Temperature, and Electrodermal Activity) through strict imputation protocols. We evaluated three model variants-Basic (clinical only), Air Pollution-added, and Social Media-integrated-while incorporating a novel Basal Module to personalize physiological baselines for each resident. Results:

SUBMITTER: Granda M 

PROVIDER: S-EPMC12840934 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature