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ORIGINAL RESEARCH article

Front. Aging Neurosci.

Sec. Alzheimer's Disease and Related Dementias

Volume 17 - 2025 | doi: 10.3389/fnagi.2025.1670609

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

Provisionally accepted
  • 1Ionian University, Corfu, Greece
  • 2The Johns Hopkins University School of Medicine, Baltimore, United States

The final, formatted version of the article will be published soon.

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. The dataset consists of a high-volume, ten-year export of raw EHR data from Epic, the Johns Hopkins (JH) Health System. 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. In this study, we utilized multimodal JH EHR data to develop a patient-based model to predict dementia onset over a five-year period. The interpretable binary classification model identified prognostic rulesets for dementia based on clinical characteristics, achieving a mean test accuracy of 0.722 (95% CI: 0.722–0.723) and an AUROC of 0.795 (95% CI: 0.794–0.795) using 5-fold cross-validation across different sample subsets. Recognizing that neurodegenerative diseases are often driven by multiple contributing factors rather than a single cause, we identify risk pathways by leveraging multimodal data and modeling their combined effects, leading to accurate dementia predictions and improved clinical interoperability.

Keywords: machine learning, dementia prediction, Alzheimer's disease, Electronic Health Records, clinical study, Cognition, case-control, prognostic

Received: 21 Jul 2025; Accepted: 02 Sep 2025.

Copyright: © 2025 Exarchos, Dimakopoulos, Lazaros, Krokidis, Vrahatis, Grammenos, Avramouli, Skolariki, Adams, MACHAIRAKI, Oh, Leoutsakos, Rosenberg, Lyketsos and VLAMOS. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Themis P. Exarchos, Ionian University, Corfu, Greece
George A Dimakopoulos, Ionian University, Corfu, Greece

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