Evidence map›Paper›PMID 41099066›Full record

ArticleFrontiers in aging neuroscience2025

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

Themis P Exarchos, George A Dimakopoulos, Konstantinos Lazaros, Marios Krokidis, Aristidis Vrahatis, Gerasimos Grammenos, Antigoni Avramouli, Konstantina Skolariki, Roy Adams, Vasiliki Mahairaki and 5 more

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In one paragraph

Article in Frontiers in aging neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Themis P ExarchosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
George A DimakopoulosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
Konstantinos LazarosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
Marios KrokidisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
Aristidis VrahatisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
Gerasimos GrammenosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
Antigoni AvramouliBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
Konstantina SkolarikiJohns Hopkins University School of Medicine, Baltimore, MD, United States.
Roy AdamsJohns Hopkins University School of Medicine, Baltimore, MD, United States.
Vasiliki MahairakiJohns Hopkins University School of Medicine, Baltimore, MD, United States.
Esther S OhJohns Hopkins University School of Medicine, Baltimore, MD, United States.
Jeannie LeoutsakosJohns Hopkins University School of Medicine, Baltimore, MD, United States.
Paul B RosenbergJohns Hopkins University School of Medicine, Baltimore, MD, United States.
Constantine G LyketsosJohns Hopkins University School of Medicine, Baltimore, MD, United States.
Panagiotis VlamosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.

Funding

Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3M
Machine learning-based methods for phenotyping dementia patients from electronic health record dataK25AG083064 · NIA · JOHNS HOPKINS UNIVERSITY · PI Roy Adams · 2023 to 2026
$561k
NIA NIH HHS K25 AG083064NIA NIH HHS P30 AG066507
6 · The paper itself

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 predict dementia onset over a five-year period. The interpretable binary classification model identified prognostic rulesets for dementia based on clinical characteristics. Results: The 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. Discussion: 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.

Indexed as

Alzheimer's diseaseclinical studycognitiondementia predictionelectronic health recordspatient-level predictionreal-world datarisk prediction

Identifiers

PMID41099066
PMCPMC12518304

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.