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

Patient-Reported Outcome Measures (PROMs) (292) Decision Support for Health Professionals (1203) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (842) New Methods (959) Research Instruments, Questionnaires, and Tools (586) Machine Learning (1491)

Published on in Vol 23, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26412, first published .
Recursive Partitioning vs Computerized Adaptive Testing to Reduce the Burden of Health Assessments in Cleft Lip and/or Palate: Comparative Simulation Study

Recursive Partitioning vs Computerized Adaptive Testing to Reduce the Burden of Health Assessments in Cleft Lip and/or Palate: Comparative Simulation Study

Recursive Partitioning vs Computerized Adaptive Testing to Reduce the Burden of Health Assessments in Cleft Lip and/or Palate: Comparative Simulation Study

Authors of this article:

Conrad J Harrison1 Author Orcid Image ;   Chris J Sidey-Gibbons2 Author Orcid Image ;   Anne F Klassen3 Author Orcid Image ;   Karen W Y Wong Riff4 Author Orcid Image ;   Dominic Furniss1 Author Orcid Image ;   Marc C Swan5 Author Orcid Image ;   Jeremy N Rodrigues6, 7 Author Orcid Image
Article Authors Cited by (1) Tweetations (6) Metrics

Journals

  1. Harrison C, Geoghegan L, Sidey-Gibbons C, Stirling P, McEachan J, Rodrigues J. Developing Machine Learning Algorithms to Support Patient-centered, Value-based Carpal Tunnel Decompression Surgery. Plastic and Reconstructive Surgery - Global Open 2022;10(4):e4279 View