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Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65028, first published .
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Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks

Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks

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;59(2):268 View
  3. Fan R, Wu X. From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital health, digital twins, and advanced materials for brain disorders. Frontiers in Bioengineering and Biotechnology 2026;14 View
  4. Overmars M, Allaart C, Bron E, Brunner La Rocca H, de Bresser J, Muller M, van Osch M, Teunissen C, Tijms B, Wolters F, Biessels G. Bayesian networks to estimate prognosis in vascular cognitive impairment and small vessel disease: Integrated analyses of interdependent contributors to multiple outcomes. Journal of Cerebral Blood Flow & Metabolism 2026 View