Risk prediction of mild cognitive impairment using electronic health record data.

ABSTRACT:

Introduction

Mild cognitive impairment (MCI) is underdiagnosed by primary care providers (PCPs), with detection rates as low as 6%-15%. Predictive models support the identification of individuals at risk, enabling timely intervention.

Methods

This retrospective study was conducted on 271,054 cognitively unimpaired and 14,501 confirmed MCI cohorts from electronic health records. A machine learning model was developed with a data-driven variable selection approach based on demographics and comorbidities.

Results

From 101 variables, 26 were chosen for the final model, achieving an overall area under the curve (AUC) of 86%. Age-stratified AUCs were 79.1% (40-49), 77.0% (50-64), 76.9% (65-79), and 74.4% (≥80), showing the highest predictive performance in younger age group

SUBMITTER: Li G 

PROVIDER: S-EPMC12644922 | biostudies-literature | 2025 Oct-Dec

REPOSITORIES: biostudies-literature