Published on in Vol 20, No 7 (2018): July

Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study

Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study

Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study

Journals

  1. Piau A, Wild K, Mattek N, Kaye J. Current State of Digital Biomarker Technologies for Real-Life, Home-Based Monitoring of Cognitive Function for Mild Cognitive Impairment to Mild Alzheimer Disease and Implications for Clinical Care: Systematic Review. Journal of Medical Internet Research 2019;21(8):e12785 View
  2. Morley J, Machado C, Burr C, Cowls J, Taddeo M, Floridi L. The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review. SSRN Electronic Journal 2019 View
  3. Topol E. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine 2019;25(1):44 View
  4. Zhu W, Xie L, Han J, Guo X. The Application of Deep Learning in Cancer Prognosis Prediction. Cancers 2020;12(3):603 View
  5. Shen X, Wang G, Kwan R, Choi K. Using Dual Neural Network Architecture to Detect the Risk of Dementia With Community Health Data: Algorithm Development and Validation Study. JMIR Medical Informatics 2020;8(8):e19870 View
  6. Cleret de Langavant L, Bayen E, Bachoud‐Lévi A, Yaffe K. Approximating dementia prevalence in population‐based surveys of aging worldwide: An unsupervised machine learning approach. Alzheimer's & Dementia: Translational Research & Clinical Interventions 2020;6(1) View
  7. Khan A, Zubair S. Longitudinal Magnetic Resonance Imaging as a Potential Correlate in the Diagnosis of Alzheimer Disease: Exploratory Data Analysis. JMIR Biomedical Engineering 2020;5(1):e14389 View
  8. Shen J, Zhang C, Jiang B, Chen J, Song J, Liu Z, He Z, Wong S, Fang P, Ming W. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Medical Informatics 2019;7(3):e10010 View
  9. Graham S, Lee E, Jeste D, Van Patten R, Twamley E, Nebeker C, Yamada Y, Kim H, Depp C. Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review. Psychiatry Research 2020;284:112732 View
  10. Luo H, Lau K, Wong G, Chan W, Mak H, Zhang Q, Knapp M, Wong I. Predicting dementia diagnosis from cognitive footprints in electronic health records: a case–control study protocol. BMJ Open 2020;10(11):e043487 View
  11. Hu M, Shu X, Yu G, Wu X, Välimäki M, Feng H. A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study. Journal of Medical Internet Research 2021;23(2):e20298 View
  12. Syed M, Syed S, Sexton K, Syeda H, Garza M, Zozus M, Syed F, Begum S, Syed A, Sanford J, Prior F. Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review. Informatics 2021;8(1):16 View
  13. Jin H, Chien S, Meijer E, Khobragade P, Lee J. Learning from Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study (Preprint). JMIR Mental Health 2021 View

Books/Policy Documents

  1. Tuena C, Chiappini M, Repetto C, Riva G. Reference Module in Neuroscience and Biobehavioral Psychology. View
  2. Tuena C, Semonella M, Fernández-Álvarez J, Colombo D, Cipresso P. P5 eHealth: An Agenda for the Health Technologies of the Future. View