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

Theme issue 2019: Health Natural Language Processing (17) Machine Learning (1501) Natural Language Processing (706) Ontologies, Classifications, and Coding (243) Dementia and Cognitive Decline (502)

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 (11) Tweetations (6) Metrics

Journals

  1. Shehzad A, Rockwood K, Stanley J, Dunn T, Howlett S. Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach. Journal of Medical Internet Research 2020;22(11):e20840 View
  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
  3. 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
  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

Books/Policy Documents

  1. Ferdib-Al-Islam , Sanim M, Islam M, Rahman S, Afzal R, Hasan K. Hybrid Intelligent Systems. View