JMIR Publications

ABSTRACT

Background:

The development and progression of chronic pain is unique to each individual, making management of the condition and prognosis challenging. Personalized digital health tools like the Manage My Pain (MMP) app can enhance pain management by providing real-time data on symptom patterns.

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 (TPS).

Methods:

Information entered into the MMP app by 160 TPS patients over a one-month period, including profile information, pain records, daily reflections, and clinical questionnaire responses, was used to generate 245 data features. A machine learning model using logistic regression with recursive feature elimination was developed 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 utilized to test the model’s performance.

Results:

Information entered into the MMP app by 160 TPS patients over a one-month period, including profile information, pain records, daily reflections, and clinical questionnaire responses, was used to generate 245 data features. A machine learning model using logistic regression with recursive feature elimination was developed 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 utilized to test the model’s performance.

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.