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

Decision Support for Health Professionals (2170) Clinical Information and Decision Making (3609) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (2337) Chronic Conditions (549) Digital Pain Assessment and Management (316)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67178, first published .
Doctor and patient review health data on a mobile app and tablet

Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach

Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach

Authors of this article:

James Skoric1, 2 Author Orcid Image ;   Anna M Lomanowska3 Author Orcid Image ;   Tahir Janmohamed2 Author Orcid Image ;   Heather Lumsden-Ruegg4 Author Orcid Image ;   Joel Katz3, 4, 5 Author Orcid Image ;   Hance Clarke3, 5 Author Orcid Image ;   Quazi Abidur Rahman6 Author Orcid Image
Article Authors Cited by (3) Tweetations (7) Metrics

Journals

  1. Freitag H, Melvin R, Godwin R, Ness T. Transitional Pain Services and Clinics: A Scoping Review of Recent Literature. Journal of Pain Research 2026;Volume 19:1 View
  2. Cai W, Ang Y, Zhou X, Li Y, Long Y, Yu Z, Bao H, Li Q. From Empirical Scales to Multimodal Data-Driven Approaches: The Role of Artificial Intelligence. AI in Neurology 2026;1(3):37 View

Books/Policy Documents

  1. Alipour D, Perepelkina O, Vinciarelli A, Janmohamed T, Nguyen B, Stumpf S. Artificial Intelligence in Medicine. View