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

Proposals (eHealth) (80) Clinical Information and Decision Making (1407) Digital Pain Assessment and Management (164) Decision Support for Health Professionals (1185) Tools, Programs and Algorithms (266) Back Pain (116) Development and Evaluation of Research Methods, Instruments and Tools (481)

Published on in Vol 10, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21804, first published .
A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study

A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study

A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study

Authors of this article:

Adele Hill1 Author Orcid Image ;   Christopher H Joyner2 Author Orcid Image ;   Chloe Keith-Jopp1, 3 Author Orcid Image ;   Barbaros Yet4 Author Orcid Image ;   Ceren Tuncer Sakar5 Author Orcid Image ;   William Marsh2 Author Orcid Image ;   Dylan Morrissey1, 3 Author Orcid Image
Article Authors Cited by (4) Tweetations (6) Metrics

Journals

  1. Laitila P, Virtanen K. Portraying probabilistic relationships of continuous nodes in Bayesian networks with ranked nodes method. Decision Support Systems 2022;154:113709 View
  2. Sadik O, Schaffer J, Land W, Xue H, Yazgan I, Kafesçiler K, Sungur M. A Bayesian Network Concept for Pain Assessment. JMIR Biomedical Engineering 2022;7(2):e35711 View
  3. Laitila P, Virtanen K. Advancing construction of conditional probability tables of Bayesian networks with ranked nodes method. International Journal of General Systems 2022;51(8):758 View
  4. Hill A, Joyner C, Keith-Jopp C, Yet B, Tuncer Sakar C, Marsh W, Morrissey D. Assessing Serious Spinal Pathology Using Bayesian Network Decision Support: Development and Validation Study. JMIR Formative Research 2023;7:e44187 View