%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 13
%N 
%P e67178
%T Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach
%A Skoric,James
%A Lomanowska,Anna M
%A Janmohamed,Tahir
%A Lumsden-Ruegg,Heather
%A Katz,Joel
%A Clarke,Hance
%A Rahman,Quazi Abidur
%K chronic pain
%K transitional pain
%K pain interference
%K machine learning
%K prediction model
%K manage my pain
%K pain app
%K clinical outcome
%K Toronto
%K Canada
%K transitional pain service
%K pain service
%K pain
%K app
%K application
%K prognosis
%K chronic pain management
%K digital health
%K digital health tool
%K pain management
%K machine learning methods
%K machine learning
%K prediction
%K machine learning models
%K logistic regression
%D 2025
%7 28.3.2025
%9 
%J JMIR Med Inform
%G English
%X 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. Objective: This study applies machine learning methods using real-world user data from the MMP app to predict clinically significant improvements in pain-related outcomes among patients at the Toronto General Hospital Transitional Pain Service. Methods: Information entered into the MMP app by 160 Transitional Pain Service patients over a 1-month period, including profile information, pain records, daily reflections, and clinical questionnaire responses, was used to extract 245 relevant variables, referred to as features, for use in a machine learning model. The machine learning model was developed using logistic regression with recursive feature elimination to predict clinically significant improvements in pain-related pain interference, assessed by the PROMIS Pain Interference 8a v1.0 questionnaire. The model was tuned and the important features were selected using the 10-fold cross-validation method. Leave-one-out cross-validation was used to test the model’s performance. Results: The model predicted patient improvement in pain interference with 79% accuracy and an area under the receiver operating characteristic curve of 0.82. It showed balanced class accuracies between improved and nonimproved patients, with a sensitivity of 0.76 and a specificity of 0.82. Feature importance analysis indicated that all MMP app data, not just clinical questionnaire responses, were key to classifying patient improvement. Conclusions: 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. 
%R 10.2196/67178
%U https://medinform.jmir.org/2025/1/e67178
%U https://doi.org/10.2196/67178