AUTHOR=Exarchos Themis P. , Dimakopoulos George A. , Lazaros Konstantinos , Krokidis Marios , Vrahatis Aristidis , Grammenos Gerasimos , Avramouli Antigoni , Skolariki Konstantina , Adams Roy , Mahairaki Vasiliki , Oh Esther S. , Leoutsakos Jeannie , Rosenberg Paul B. , Lyketsos Constantine G. , Vlamos Panagiotis 

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

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 17 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1670609

DOI=10.3389/fnagi.2025.1670609

ISSN=1663-4365

ABSTRACT=IntroductionThis 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.MethodsThe 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 predict dementia onset over a five-year period. The interpretable binary classification model identified prognostic rulesets for dementia based on clinical characteristics.ResultsThe model achieved 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.DiscussionRecognizing 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.