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

Big Data Analytics for Elder Care (20) Machine Learning (1401) Electronic Health Records (971) Clinical Informatics (919) Artificial Intelligence (1216) Falls Prevention in the Elderly (64) Assisted Living for the Elderly and Nursing Home Care (147) Clinical Information and Decision Making (1340) Nursing in a Long-Term Care Facility / Nursing Home for the Elderly (24)

Published on in Vol 5, No 2 (2022): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35373, first published .
Predicting Falls in Long-term Care Facilities: Machine Learning Study

Predicting Falls in Long-term Care Facilities: Machine Learning Study

Predicting Falls in Long-term Care Facilities: Machine Learning Study

Authors of this article:

Rahul Thapa1 Author Orcid Image ;   Anurag Garikipati1 Author Orcid Image ;   Sepideh Shokouhi1 Author Orcid Image ;   Myrna Hurtado1 Author Orcid Image ;   Gina Barnes1 Author Orcid Image ;   Jana Hoffman1 Author Orcid Image ;   Jacob Calvert1 Author Orcid Image ;   Lynne Katzmann2 Author Orcid Image ;   Qingqing Mao1 Author Orcid Image ;   Ritankar Das1 Author Orcid Image
Article Authors Cited by (13) Tweetations (2) Metrics

Journals

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  2. Zhang K, Liu W, Zhang J, Li Z, Liu J. A Fall Risk Assessment Model for Community-Dwelling Elderly Individuals Based on Gait Parameters. IEEE Access 2023;11:120857 View
  3. Choi J, Choi E, Park D. In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study. BMC Medical Informatics and Decision Making 2023;23(1) View
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  5. Thapa R, Garikipati A, Ciobanu M, Singh N, Browning E, DeCurzio J, Barnes G, Dinenno F, Mao Q, Das R. Machine Learning Differentiation of Autism Spectrum Sub-Classifications. Journal of Autism and Developmental Disorders 2023 View
  6. Al Abiad N, van Schooten K, Renaudin V, Delbaere K, Robert T. Association of Prospective Falls in Older People With Ubiquitous Step-Based Fall Risk Parameters Calculated From Ambulatory Inertial Signals: Secondary Data Analysis. JMIR Aging 2023;6:e49587 View
  7. Woodman R, Mangoni A. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future. Aging Clinical and Experimental Research 2023;35(11):2363 View
  8. Han E, Kharrazi H, Shi L. Identifying Predictors of Nursing Home Admission by Using Electronic Health Records and Administrative Data: Scoping Review. JMIR Aging 2023;6:e42437 View
  9. Cheligeer C, Wu G, Lee S, Pan J, Southern D, Martin E, Sapiro N, Eastwood C, Quan H, Xu Y. BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study. JMIR Medical Informatics 2024;12:e48995 View
  10. Tago M, Hirata R, Katsuki N, Nakatani E, Tokushima M, Nishi T, Shimada H, Yaita S, Saito C, Amari K, Kurogi K, Oda Y, Shikino K, Ono M, Yoshimura M, Yamashita S, Tokushima Y, Aihara H, Fujiwara M, Yamashita S. Validation and Improvement of the Saga Fall Risk Model: A Multicenter Retrospective Observational Study. Clinical Interventions in Aging 2024;Volume 19:175 View
  11. Adelson R, Garikipati A, Zhou Y, Ciobanu M, Tawara K, Barnes G, Singh N, Mao Q, Das R. Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes. Diagnostics 2024;14(11):1152 View
  12. Solaiman B. Legal and Ethical Considerations of Artificial Intelligence for Residents in Post-Acute and Long-Term Care. Journal of the American Medical Directors Association 2024;25(9):105105 View
  13. Shao L, Wang Z, Xie X, Xiao L, Shi Y, Wang Z, Zhang J. Development and External Validation of a Machine Learning–based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study. Journal of the American Medical Directors Association 2024:105169 View