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

Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (834) Cardiac Rehabilitation (85)

Published on in Vol 22, No 1 (2020): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12509, first published .
Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study

Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study

Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study

Authors of this article:

Linda Peute1 Author Orcid Image ;   Thom Scheeve2 Author Orcid Image ;   Monique Jaspers1 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Yu C, Lin Y, Lin C, Lin S, Wu J, Chang S. Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach. Journal of Medical Internet Research 2020;22(6):e18585 View
  2. Tennant A, Küçükdeveci A. Application of the Rasch measurement model in rehabilitation research and practice: early developments, current practice, and future challenges. Frontiers in Rehabilitation Sciences 2023;4 View
  3. Zhou J, Zha F, Liu F, Wan L, Zhou M, Long J, Chen M, Xue K, Wang Y. Reliability and validity of a graphical computerized adaptive test Longshi scale for rapid assessment of activities of daily living in stroke survivors. Scientific Reports 2024;14(1) View
  4. Sheng B, Zhang S, Gao Y, Xia S, Zhu Y, Yan J. Elucidating the influence of familial interactions on geriatric depression: A comprehensive nationwide multi-center investigation leveraging machine learning. Acta Psychologica 2024;246:104274 View