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

Research Instruments, Questionnaires, and Tools (418) New Methods (666) Big Data (169) Machine Learning (1007) Big Data Analytics for Elder Care (12) Falls Prevention in the Elderly (54) Geroinformatics and Electronic Clinical Information/Decision Making in Geriatrics (14) Health Services Research and Health Care Utilization in Older Patients (35) Intensive Care Unit (ICU) (88)

Published on in Vol 22 , No 6 (2020) :June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16213, first published .
Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach

Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach

Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach

Authors of this article:

Li-Ning Peng 1, 2, 3 Author Orcid Image ;   Fei-Yuan Hsiao 4, 5, 6 Author Orcid Image ;   Wei-Ju Lee 1, 2, 3, 7 Author Orcid Image ;   Shih-Tsung Huang 4 Author Orcid Image ;   Liang-Kung Chen 1, 2, 3 Author Orcid Image
Article Authors Cited by (12) Tweetations (8) Metrics

Journals

  1. Chen L. Gerontechnology and artificial intelligence: Better care for older people. Archives of Gerontology and Geriatrics 2020;91:104252 View
  2. Chen L. Machine Learning Improves Analysis of Multi-Omics Data in Aging Research and Geroscience. Archives of Gerontology and Geriatrics 2021;93:104360 View
  3. Majnarić L, Babič F, O’Sullivan S, Holzinger A. AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity. Journal of Clinical Medicine 2021;10(4):766 View
  4. Akbari G, Nikkhoo M, Wang L, Chen C, Han D, Lin Y, Chen H, Cheng C. Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach. Sensors 2021;21(12):4017 View
  5. Pan Y, Meng L, Chen H, Chen L, Hsiao F. Impact of Frailty on Survivals of Prostate Cancer Patients Treated with Radiotherapy. Archives of Gerontology and Geriatrics 2022;100:104651 View
  6. Sarwar T, Jimeno Yepes A, Zhang X, Chan J, Hudson I, Evans S, Cavedon L. Development and validation of retrospective electronic frailty index using operational data of aged care homes. BMC Geriatrics 2022;22(1) View
  7. Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2—Data From Nonwearables. Value in Health 2022;25(12):2053 View
  8. Yu P, Hsu C, Lee W, Liang C, Chou M, Lin M, Hsiao F, Peng L, Chen L. Muscle‐to‐fat ratio identifies functional impairments and cardiometabolic risk and predicts outcomes: biomarkers of sarcopenic obesity. Journal of Cachexia, Sarcopenia and Muscle 2022;13(1):368 View
  9. Zhang X, Campomizzi A, Lebrun‐Southcott Z. Predicting population trends of birds worldwide with big data and machine learning. Ibis 2022;164(3):750 View
  10. Zhang X, Gao X, Wu D, Xu Z, Wang H. The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework. Sustainability 2021;13(21):11587 View
  11. Gray C, Lesser G, Guo Y, Shah S, Allen S, Wilkinson L, Sims O. COVID-19 Vaccination Intention and Factors Associated with Hesitance and Resistance in the Deep South: Montgomery, Alabama. Tropical Medicine and Infectious Disease 2022;7(11):331 View
  12. Moguilner S, Knight S, Davis J, O’Halloran A, Kenny R, Romero-Ortuno R. The Importance of Age in the Prediction of Mortality by a Frailty Index: A Machine Learning Approach in the Irish Longitudinal Study on Ageing. Geriatrics 2021;6(3):84 View