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Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59396, first published .
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A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation

A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation

Journals

  1. Yongcharoenchaiyasit K, Arwatchananukul S, Hristov G, Temdee P. Enhanced Multi-Model Machine Learning-Based Dementia Detection Using a Data Enrichment Framework: Leveraging the Blessing of Dimensionality. Bioengineering 2025;12(6):592 View
  2. Oravecz Z, Sliwinski M, Kim S, Williams L, Katz M, Vandekerckhove J. Partially Observable Predictor Models for Identifying Cognitive Markers. Computational Brain & Behavior 2025;8(3):410 View
  3. Dai C, Zhao W, Yang D, Fan G, Meng Q, Yang H, Xie L, Zhang Y, Zha X. Testing the Role of Depression in the Relationship Between Socioeconomic Status and Cognitive Function Among Older Chinese Adults: Findings From the Anhui Healthy Longevity Survey. Alpha Psychiatry 2025;26(5) View
  4. Sohn M, Yang J, Lee J, Choi D. Predictive factors for dementia among older adults in South Korea: an interpretable machine learning analysis. Alzheimer's Research & Therapy 2025;17(1) View
  5. Al-Hindawi F, Wu T, Wen Y, Serhan P, Forzani E, Tsow F, Geda Y. Leveraging Naturalistic Driving Digital Biomarkers for Early Mild Cognitive Impairment Detection: Deep Learning Strategies. JMIR Medical Informatics 2026;14:e83622 View
  6. Ahmed E, Rahman A, Yusuf A, Hassan M, Moureen A, Nur Uddin M, Hakim M. Emulating clinician-assigned diagnostic patterns of Alzheimer's disease in a tertiary neurology hospital using interpretable Bayesian machine learning. Journal of Alzheimer's Disease Reports 2026;10 View