Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach
This study demonstrates that data from a digital health app can be integrated with clinical questionnaire responses in a machine learning model to effectively predict which chronic pain patients will show clinically significant improvement. The findings emphasize the potential of machine learning methods in real-world clinical settings to improve personalized treatment plans and patient outcomes.
Authors
- Anna M. Lomanowska (ORCID: https://orcid.org/0000-0003-0847-6153)
- Joel D Katz (ORCID: https://orcid.org/0000-0002-8686-447X)
- Quazi Abidur Rahman (ORCID: https://orcid.org/0000-0001-9031-676X)
- James Skoric (ORCID: https://orcid.org/0000-0003-3418-6635)
- Hance Clarke (ORCID: https://orcid.org/0000-0003-4975-3823)
- Tahir Janmohamed (ORCID: https://orcid.org/0000-0001-7006-4358)
- Heather Lumsden-Ruegg (ORCID: https://orcid.org/0009-0004-0715-1941)
Institutions
- University Health Network (CA)
- University of Toronto (CA)
- York University (CA)
- Toronto General Hospital (CA)
- McGill University (CA)
- Trent University (CA)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-03-28
- DOI
- https://doi.org/10.2196/67178
- Citations
- 1
- Primary Topic
- Musculoskeletal pain and rehabilitation
- Type
- article
- Field-Weighted Citation Impact
- 1.39