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

Machine Learning (1401) Computer-Aided Diagnosis (102) Dementia and Cognitive Decline (429)

Published on in Vol 8, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20995, first published .
Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Authors of this article:

Debbie Rankin1 Author Orcid Image ;   Michaela Black1 Author Orcid Image ;   Bronac Flanagan1 Author Orcid Image ;   Catherine F Hughes2 Author Orcid Image ;   Adrian Moore3 Author Orcid Image ;   Leane Hoey2 Author Orcid Image ;   Jonathan Wallace4 Author Orcid Image ;   Chris Gill2 Author Orcid Image ;   Paul Carlin5 Author Orcid Image ;   Anne M Molloy6 Author Orcid Image ;   Conal Cunningham7 Author Orcid Image ;   Helene McNulty2 Author Orcid Image
Article Authors Cited by (7) Tweetations (7) Metrics

Journals

  1. Renn B, Schurr M, Zaslavsky O, Pratap A. Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care. Frontiers in Psychiatry 2021;12 View
  2. Li X, Pei Y, Zhao Y, Song H, Zhao J, Yan L, He H, Lu S, Yan X. Memristors based on carbon dots for learning activities in artificial biosynapse applications. Materials Chemistry Frontiers 2022;6(8):1098 View
  3. Kim G, Park K, Kim Y, Jeong G. Increased Hippocampal-Inferior Temporal Gyrus White Matter Connectivity following Donepezil Treatment in Patients with Early Alzheimer’s Disease: A Diffusion Tensor Probabilistic Tractography Study. Journal of Clinical Medicine 2023;12(3):967 View
  4. Bohn L, Drouin S, McFall G, Rolfson D, Andrew M, Dixon R. Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer’s disease spectrum: a COMPASS-ND study. BMC Geriatrics 2023;23(1) View
  5. Ariza M, Béjar J, Barrué C, Cano N, Segura B, Bernia J, Arauzo V, Balague-Marmaña M, Pérez-Pellejero C, Cañizares S, Muñoz J, Caballero J, Carnes-Vendrell A, Piñol-Ripoll G, Gonzalez-Aguado E, Riera-Pagespetit M, Forcadell-Ferreres E, Reverte-Vilarroya S, Forné S, Muñoz-Padros J, Bartes-Plan A, Muñoz-Moreno J, Prats-Paris A, Pons I, Molina J, Casas-Henanz L, Castejon J, Mas M, Jodrà A, Lozano M, Garzon T, Cullell M, Vega S, Alsina S, Maldonado-Belmonte M, Vazquez-Rivera S, García-Cabello E, Molina Y, Navarro S, Baillès E, Cortés C, Junqué C, Garolera M. Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19. European Archives of Psychiatry and Clinical Neuroscience 2024 View

Books/Policy Documents

  1. Hentosh L, Savchyn V, Kravchenko O. Advances in Computer Science for Engineering and Education VI. View