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

Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (632) Clinical Information and Decision Making (1615) Dementia and Cognitive Decline (574) Machine Learning (1697)

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

Authors of this article:

Laurent Cleret de Langavant1, 2, 3, 4, 5 Author Orcid Image ;   Eleonore Bayen5, 6, 7, 8 Author Orcid Image ;   Kristine Yaffe5, 9 Author Orcid Image
Article Authors Cited by (54) Tweetations (72) Metrics

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. JMIR Mental Health 2021;8(5):e27113 View
  14. Froud R, Hansen S, Ruud H, Foss J, Ferguson L, Fredriksen P. Relative Performance of Machine Learning and Linear Regression in Predicting Quality of Life and Academic Performance of School Children in Norway: Data Analysis of a Quasi-Experimental Study. Journal of Medical Internet Research 2021;23(7):e22021 View
  15. Watson N, Fernandez C. Artificial intelligence and sleep: Advancing sleep medicine. Sleep Medicine Reviews 2021;59:101512 View
  16. Dashwood M, Churchhouse G, Young M, Kuruvilla T. Artificial intelligence as an aid to diagnosing dementia: an overview. Progress in Neurology and Psychiatry 2021;25(3):42 View
  17. Rostamzadeh N, Abdullah S, Sedig K, Garg A, McArthur E. VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification. Information 2021;12(9):344 View
  18. Brogi S, Calderone V. Artificial Intelligence in Translational Medicine. International Journal of Translational Medicine 2021;1(3):223 View
  19. Hariry R, Barenji R, Paradkar A. Towards Pharma 4.0 in clinical trials: A future-orientated perspective. Drug Discovery Today 2022;27(1):315 View
  20. Liu L, Wan H, Liu L, Wang J, Tang Y, Cui S, Li Y. Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer. Diagnostics 2023;13(4):748 View
  21. Bucholc M, Titarenko S, Ding X, Canavan C, Chen T. A hybrid machine learning approach for prediction of conversion from mild cognitive impairment to dementia. Expert Systems with Applications 2023;217:119541 View
  22. Santander-Cruz Y, Salazar-Colores S, Paredes-García W, Guendulain-Arenas H, Tovar-Arriaga S. Semantic Feature Extraction Using SBERT for Dementia Detection. Brain Sciences 2022;12(2):270 View
  23. Xu H, Intrator O, Culakova E, Bowblis J. Changing landscape of nursing homes serving residents with dementia and mental illnesses. Health Services Research 2022;57(3):505 View
  24. Ahmadzadeh M, Cosco T, Best J, Christie G, DiPaola S, Rashid T. Predictors of the rate of cognitive decline in older adults using machine learning. PLOS ONE 2023;18(3):e0280029 View
  25. Cleret de Langavant L, Roze E, Petit A, Tressières B, Gharbi‐Meliani A, Chaumont H, Michel P, Bachoud‐Lévi A, Remy P, Edragas R, Lannuzel A. Annonaceae Consumption Worsens Disease Severity and Cognitive Deficits in Degenerative Parkinsonism. Movement Disorders 2022;37(12):2355 View
  26. Ge X, Cui K, Liu L, Qin Y, Cui J, Han H, Luo Y, Yu H. Screening and predicting progression from high-risk mild cognitive impairment to Alzheimer’s disease. Scientific Reports 2021;11(1) View
  27. Xu Q, Zou K, Deng Z, Zhou J, Dang X, Zhu S, Liu L, Fang C. A Study of Dementia Prediction Models Based on Machine Learning with Survey Data of Community-Dwelling Elderly People in China. Journal of Alzheimer's Disease 2022;89(2):669 View
  28. 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
  29. Javeed A, Dallora A, Berglund J, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. Journal of Medical Systems 2023;47(1) View
  30. Twait E, Andaur Navarro C, Gudnason V, Hu Y, Launer L, Geerlings M. Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study. BMC Medical Informatics and Decision Making 2023;23(1) View
  31. Khera P, Kumar N. Decision support framework for predicting rate of gait recovery with optimized treatment planning. Expert Systems with Applications 2024;238:121721 View
  32. Garcia Valencia O, Thongprayoon C, Jadlowiec C, Mao S, Miao J, Cheungpasitporn W. Enhancing Kidney Transplant Care through the Integration of Chatbot. Healthcare 2023;11(18):2518 View
  33. Ren Y, Shahbaba B, Stark C. Improving clinical efficiency in screening for cognitive impairment due to Alzheimer's. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 2023;15(4) View
  34. Gharbi-Meliani A, Husson F, Vandendriessche H, Bayen E, Yaffe K, Bachoud-Lévi A, Cleret de Langavant L. Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis. Alzheimer's Research & Therapy 2023;15(1) View
  35. Kamruzzaman M, Heavey J, Song A, Bielskas M, Bhattacharya P, Madden G, Klein E, Deng X, Vullikanti A. Improving Risk Prediction of Methicillin-Resistant Staphylococcus aureus Using Machine Learning Methods With Network Features: Retrospective Development Study. JMIR AI 2024;3:e48067 View
  36. Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women’s Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. Journal of Clinical Medicine 2024;13(4):1061 View
  37. Klee M, Langa K, Leist A. Performance of probable dementia classification in a European multi-country survey. Scientific Reports 2024;14(1) View
  38. Ran W, Yu Q. Data-driven clustering approach to identify novel clusters of high cognitive impairment risk among Chinese community-dwelling elderly people with normal cognition: A national cohort study. Journal of Global Health 2024;14 View
  39. Noroozi M, Gholami M, Sadeghsalehi H, Behzadi S, Habibzadeh A, Erabi G, Sadatmadani S, Diyanati M, Rezaee A, Dianati M, Rasoulian P, Khani Siyah Rood Y, Ilati F, Hadavi S, Arbab Mojeni F, Roostaie M, Deravi N. Machine and deep learning algorithms for classifying different types of dementia: A literature review. Applied Neuropsychology: Adult 2024:1 View
  40. Kolsanova A, Chechko S, Kira E, Shamshatdinova A. Cervical screening and artificial intelligence. Science and Innovations in Medicine 2024;9(4):246 View
  41. Oliveira M, Mendes F, Martins M, Cardoso P, Fonseca J, Mascarenhas T, Saraiva M. The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects. Diagnostics 2025;15(3):274 View
  42. Wang B, Xie R, Qi W, Yao J, Shi Y, Lou X, Dong C, Zhu X, Wang B, He D, Chen Y, Cao S. Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study. BMJ Open 2025;15(2):e092293 View
  43. Nappo R, Simeoli R. Il contributo dell'intelligenza artificiale nella diagnosi dei disturbi neurodegenerativi. RIVISTA SPERIMENTALE DI FRENIATRIA 2024;148(3):131 View
  44. Börsch-Supan A, Douhou S, Otero M, Tawiah B. Harmonized prevalence estimates of dementia in Europe vary strongly with childhood education. Scientific Reports 2025;15(1) View

Books/Policy Documents

  1. Tuena C, Chiappini M, Repetto C, Riva G. Comprehensive Clinical 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
  3. Morley J, Machado C, Burr C, Cowls J, Joshi I, Taddeo M, Floridi L. Ethics, Governance, and Policies in Artificial Intelligence. View
  4. Watson N, Goldstein C, Rusk S, Fernandez C. Encyclopedia of Sleep and Circadian Rhythms. View
  5. Bian C, Khan S, Mihailidis A. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. View

Conference Proceedings

  1. Kennedy R, Salekshahrezaee Z, Khoshgoftaar T. 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI). Unsupervised Anomaly Detection of Class Imbalanced Cognition Data Using an Iterative Cleaning Method View
  2. Jena S, George S, Ponraj D. 2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC). An Investigation on Pre-processing Techniques and Image Datasets for Lung Cancer Classification View
  3. Kennedy R, Khoshgoftaar T. 2024 IEEE International Conference on Information Reuse and Integration for Data Science (IRI). Synthesizing Class Labels for Balanced and Highly Imbalanced Cognition Data View
  4. Walauskis M, Khoshgoftaar T. 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). Confident Labels: A Novel Approach to New Class Labeling and Evaluation on Highly Imbalanced Data View
  5. Walauskis M, Khoshgoftaar T. 2024 International Conference on Machine Learning and Applications (ICMLA). New Class Labeling and Evaluation Methodology for Balanced and Highly Imbalanced Data View