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

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