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

Journal: JMIR medical informatics
PMID: 40153542

Abstract

BACKGROUND: Chronic pain is a complex condition that affects more than a quarter of people worldwide. The development and progression of chronic pain are unique to each individual due to the contribution of interacting biological, psychological, and social factors. The subjective nature of the experience of chronic pain can make its clinical assessment and prognosis challenging. Personalized digital health apps, such as Manage My Pain (MMP), are popular pain self-tracking tools that can also be leveraged by clinicians to support patients. Recent advances in machine learning technologies open an opportunity to use data collected in pain apps to make predictions about a patient's prognosis.

Authors

  • James Skoric
  • Anna M Lomanowska
    Transitional Pain Service, Department of Anesthesia and Pain Management, Toronto General Hospital, University Health Network, Toronto, ON, Canada.
  • Tahir Janmohamed
    ManagingLife, Inc, Toronto, ON, Canada.
  • Heather Lumsden-Ruegg
    Department of Psychology, York University, Toronto, ON, Canada.
  • Joel Katz
    School of Kinesiology & Health Science, York University, Toronto, ON, Canada.
  • Hance Clarke
    Department of Anesthesia and Pain Management, Toronto General Hospital, Toronto, ON, Canada.
  • Quazi Abidur Rahman
    Centre for Disease Modelling, Department of Mathematics and Statistics, York University, Toronto, ON, Canada.

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