Five-year dementia prediction and decision support system based on real-world data.

ABSTRACT:

Introduction

This work presents a machine learning (ML) based risk prediction model for Alzheimer's disease and related dementias, utilizing real-world electronic health record (EHR) clinical data. While significant research has been conducted on dementia risk prediction, most studies rely on volunteer-based research cohorts rather than real-world clinical data. Using raw EHR data offers more realistic insights but poses challenges due to the extensive effort required to convert real-world EHR clinical data into a decision support system for daily clinical use.

Methods

The dataset consists of a high-volume, ten-year export of raw EHR data from Epic, the Johns Hopkins (JH) Health System. In this study, we utilized multimodal JH EHR data to develop a patient-based model to pre

SUBMITTER: Exarchos TP 

PROVIDER: S-EPMC12518304 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature