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

Journals

  1. Leme D, de Oliveira C, Lipsitz L. Machine Learning Models to Predict Future Frailty in Community-Dwelling Middle-Aged and Older Adults: The ELSA Cohort Study. The Journals of Gerontology: Series A 2023;78(11):2176 View
  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
  4. Fan L, Zhang J, Wang F, Liu S, Lin T. Exploring outdoor activity limitation (OAL) factors among older adults using interpretable machine learning. Aging Clinical and Experimental Research 2023;35(9):1955 View
  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