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

Research Instruments, Questionnaires, and Tools (576) New Methods (944) Big Data (240) Machine Learning (1465) Big Data Analytics for Elder Care (22) Falls Prevention in the Elderly (69) Geroinformatics and Electronic Clinical Information/Decision Making in Geriatrics (20) Health Services Research and Health Care Utilization in Older Patients (86) Intensive Care Unit (ICU) (112)

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 Peng1, 2, 3 Author Orcid Image ;   Fei-Yuan Hsiao4, 5, 6 Author Orcid Image ;   Wei-Ju Lee1, 2, 3, 7 Author Orcid Image ;   Shih-Tsung Huang4 Author Orcid Image ;   Liang-Kung Chen1, 2, 3 Author Orcid Image
Article Authors Cited by (25) Tweetations (5) 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
  13. Rozenberg D, Singer L. Predicting outcomes in lung transplantation: From tea leaves to ChatGPT. The Journal of Heart and Lung Transplantation 2023;42(7):905 View
  14. Chen L. A new chapter in aging research: The launch of Archives of Gerontology and Geriatrics Plus. Archives of Gerontology and Geriatrics 2024;116:105258 View
  15. Lin Y, Lin H, Chen L, Hsiao F. Unveiling the multifaceted nexus of subjective aging, biological aging, and chronological age: Findings from a nationally representative cohort study. Archives of Gerontology and Geriatrics 2024;117:105164 View
  16. Leghissa M, Carrera Á, Iglesias C. Machine learning approaches for frailty detection, prediction and classification in elderly people: A systematic review. International Journal of Medical Informatics 2023;178:105172 View
  17. Liu Q, Yang L, Shi Z, Yu J, Si H, Jin Y, Bian Y, Li Y, Ji L, Qiao X, Wang W, Liu H, Zhang M, Wang C. Development and validation of a preliminary clinical support system for measuring the probability of incident 2-year (pre)frailty among community-dwelling older adults: A prospective cohort study. International Journal of Medical Informatics 2023;177:105138 View
  18. Chen L. A new chapter in aging research: The launch of Archives of Gerontology and Geriatrics Plus. Archives of Gerontology and Geriatrics Plus 2024;1(1):100001 View
  19. Bohn L, Drouin S, McFall G, Rolfson D, Andrew M, Dixon R. Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer’s disease spectrum: a COMPASS-ND study. BMC Geriatrics 2023;23(1) View
  20. Rafferty J, Lee A, Lyons R, Akbari A, Peek N, Jalali-najafabadi F, Ba Dhafari T, Lyons J, Watkins A, Bailey R, Wulandari R. Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study. PLOS ONE 2023;18(12):e0295300 View
  21. Liu Q, Huang Y, Gao S, Wang B, Li Y, Si H, Zhou W, Yu J, Chen H, Wang C. Joint trajectories of physical frailty and social frailty and associations with adverse outcomes: A prospective cohort study. Archives of Gerontology and Geriatrics 2024;122:105406 View
  22. Anthonimuthu D, Hejlesen O, Zwisler A, Udsen F. Application of Machine Learning in Multimorbidity Research: Protocol for a Scoping Review. JMIR Research Protocols 2024;13:e53761 View
  23. Liu Q, Si H, Li Y, Zhou W, Yu J, Bian Y, Wang C. Development and validation of a risk scoring tool for predicting incident reversible cognitive frailty among community‐dwelling older adults: A prospective cohort study. Geriatrics & Gerontology International 2024;24(9):874 View
  24. Liu Q, Si H, Li Y, Zhou W, Yu J, Bian Y, Wang C. Development and Validation of Prediction Models for Incident Reversible Cognitive Frailty Based on Social-Ecological Predictors Using Generalized Linear Mixed Model and Machine Learning Algorithms: A Prospective Cohort Study. Journal of Applied Gerontology 2024 View
  25. Bai C, Mardini M. Advances of artificial intelligence in predicting frailty using real-world data: A scoping review. Ageing Research Reviews 2024;101:102529 View