Comprehensive Machine Learning-Based Prediction Model for Delirium Risk in Older Patients with Dementia: Risk Factors Identification.

Journal: Clinical interventions in aging
Published Date:

Abstract

BACKGROUND: Delirium superimposed on dementia (DSD) is a severe complication in older adults with dementia, marked by fluctuating cognition, inattention, and altered consciousness. Detection is challenging due to symptom overlap, yet it contributes to cognitive decline, prolonged hospitalization, and increased mortality. Identifying key risk factors and developing an accurate prediction model is crucial for timely intervention. This study aimed to establish a machine learning-based model to predict delirium risk, focusing on significant predictors to aid clinical decision-making.

Authors

  • Qifan Xiao
    General Practice, International Department, China-Japan Friendship Hospital, Beijing, People's Republic of China.
  • Shirui Zhou
    Graduate School, Beijing University of Chinese Medicine, Beijing, People's Republic of China.
  • Bin Tang
    Basic Medical College , Southwest Medical University , Luzhou , Sichuan , China.
  • Yuqing Zhu
    Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America.