Accessibility settings

This paper is in the following e-collection/theme issue:

Artificial Intelligence (4590) Clinical Information and Decision Making (3582) Dementia and Cognitive Decline (869) Machine Learning (3082) Digital Biomarkers and Digital Phenotyping (424) Precision Medicine (425)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65028, first published .
Active senior man jogging on a park path with a glowing network overlay

Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks

Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks

Authors of this article:

Antti Etholén1 Author Orcid Image ;   Teemu Roos2 Author Orcid Image ;   Mirja Hänninen1, 3 Author Orcid Image ;   Ioanna Bouri2 Author Orcid Image ;   Jenni Kulmala4, 5, 6 Author Orcid Image ;   Ossi Rahkonen1 Author Orcid Image ;   Anne Kouvonen7, 8 Author Orcid Image ;   Tea Lallukka1 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Zhang M, Zhong M, Cheng Y, Zhang T. Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study. JMIR Medical Informatics 2025;13:e74940 View
  2. García-Rudolph A, Sánchez-Carrión R, Cisek K, Nguyen T, Teixido-Font C, Opisso E, O'Connor R, Onose G, Cedersund G. Incomplete Cognitive Assessment in Post-Acute Stroke Rehabilitation: Recovering Clinically Interpretable Relationships from Routine Neuropsychological Data. NeuroRehabilitation 2026 View