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

Theme issue 2019: Health Natural Language Processing (17) Machine Learning (1984) Natural Language Processing (873) Ontologies, Classifications, and Coding (310) Dementia and Cognitive Decline (658)

Published on in Vol 8, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17819, first published .
Predicting Onset of Dementia Using Clinical Notes and Machine Learning: Case-Control Study

Predicting Onset of Dementia Using Clinical Notes and Machine Learning: Case-Control Study

Predicting Onset of Dementia Using Clinical Notes and Machine Learning: Case-Control Study

Authors of this article:

Christopher A Hane1 Author Orcid Image ;   Vijay S Nori1 Author Orcid Image ;   William H Crown1 Author Orcid Image ;   Darshak M Sanghavi1 Author Orcid Image ;   Paul Bleicher1 Author Orcid Image
Article Authors Cited by (19) Tweetations (6) Metrics

Journals

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  2. Maclagan L, Abdalla M, Harris D, Stukel T, Chen B, Candido E, Swartz R, Iaboni A, Jaakkimainen R, Bronskill S. Can Patients with Dementia Be Identified in Primary Care Electronic Medical Records Using Natural Language Processing?. Journal of Healthcare Informatics Research 2023;7(1):42 View
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  4. Zolnoori M, Barrón Y, Song J, Noble J, Burgdorf J, Ryvicker M, Topaz M. HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare. International Journal of Medical Informatics 2023;177:105146 View
  5. Walling A, Pevnick J, Bennett A, Vydiswaran V, Ritchie C. Dementia and electronic health record phenotypes: a scoping review of available phenotypes and opportunities for future research. Journal of the American Medical Informatics Association 2023;30(7):1333 View
  6. Cho E, Kim S, Heo S, Shin J, Hwang S, Kwon E, Lee S, Kim S, Kang B. Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation. Scientific Reports 2023;13(1) View
  7. Bucholc M, James C, Khleifat A, Badhwar A, Clarke N, Dehsarvi A, Madan C, Marzi S, Shand C, Schilder B, Tamburin S, Tantiangco H, Lourida I, Llewellyn D, Ranson J. Artificial intelligence for dementia research methods optimization. Alzheimer's & Dementia 2023;19(12):5934 View
  8. Ryvicker M, Barrón Y, Song J, Zolnoori M, Shah S, Burgdorf J, Noble J, Topaz M. Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer’s Disease and Related Dementias. Journal of Applied Gerontology 2024;43(10):1461 View
  9. Zhou J, Liu W, Zhou H, Lau K, Wong G, Chan W, Zhang Q, Knapp M, Wong I, Luo H. Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study. The Lancet Regional Health - Western Pacific 2024;46:101060 View
  10. Prakash R, Dupre M, Østbye T, Xu H. Extracting Critical Information from Unstructured Clinicians’ Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study. JMIR Aging 2024;7:e57926 View
  11. Kumar R, T. M, Shetty S, Prabhu O. Leveraging Explainable AI for Dementia Classification: A Machine Learning Approach. International Journal of Applied Sciences & Development 2025;4:15 View
  12. Shakeri A, Farmanbar M. Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 2025;17(1) View
  13. Kramer M, Cutty M, Knox S, Alekseyenko A, Mollalo A. Rural–urban disparities of Alzheimer's disease and related dementias: A scoping review. Alzheimer's & Dementia: Translational Research & Clinical Interventions 2025;11(1) View
  14. Wei R, Buss S, Milde R, Fernandes M, Sumsion D, Davis E, Kong W, Xiong Y, Veltink J, Rao S, Westover T, Petersen L, Turley N, Singh A, Das S, Junior V, Ghanta M, Gupta A, Kim J, Lam A, Stone K, Mignot E, Hwang D, Trotti L, Clifford G, Katwa U, Thomas R, Mukerji S, Zafar S, Westover M, Sun H. Automated phenotyping of mild cognitive impairment and Alzheimer’s disease and related dementias using electronic health records. International Journal of Medical Informatics 2025;200:105917 View
  15. Knox S, Aghamoosa S, Heider P, Cutty M, Wright A, Scherbakov D, Hood G, Nolin S, Obeid J. AI approaches for phenotyping Alzheimer's disease and related dementias using electronic health records. Alzheimer's & Dementia: Translational Research & Clinical Interventions 2025;11(2) View
  16. Leng Y, He Y, Amini S, Magdamo C, Paschalidis I, Mukerji S, Moura L, Westover M, Vranceanu A, Ritchie C, Blacker D, Dickson J, Das S. A GPT-4o-powered framework for identifying cognitive impairment stages in electronic health records. npj Digital Medicine 2025;8(1) View
  17. Li R, Wang X, Berlowitz D, Mez J, Lin H, Yu H. CARE-AD: a multi-agent large language model framework for Alzheimer’s disease prediction using longitudinal clinical notes. npj Digital Medicine 2025;8(1) View

Books/Policy Documents

  1. Ferdib-Al-Islam , Sanim M, Islam M, Rahman S, Afzal R, Hasan K. Hybrid Intelligent Systems. View
  2. Astell A, Watermeyer T, Semple J. Early Detection in Alzheimer' s Disease. View