Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation.

ABSTRACT:

Background

Delirium frequently complicates elderly chronic kidney disease (CKD) patients due to multifactorial vulnerability. Early detection in geriatric intensive care unit (ICU) settings is challenged by traditional assessments' communication deficits. Machine learning refines predictions through multidimensional pattern recognition. We develop an interpretable online tool for timely high-risk delirium identification, guiding interventions to enhance outcomes.

Methods

Elderly chronic kidney disease patients were selected using International Classification of Diseases (ICD) codes, with delirium defined per Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Baseline traits, vitals, lab parameters, comorbidities, and clinical scores were collected. Dual featu

SUBMITTER: Gao L 

PROVIDER: S-EPMC12659614 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature