Hybrid Statistical and Mechanistic Mathematical Model Guides Mobile Health Intervention for Chronic Pain

Hybrid Statistical and Mechanistic Mathematical Model Guides Mobile Health Intervention for Chronic Pain

The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of "big data" sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic suffering in the sickle cell disease (SCD) community.

There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. In this study, we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. Pilot testing of our approach suggests that it has significant potential to well predict pain dynamics, given patients reported pain levels and medication usages. With more abundant data, our hybrid approach should allow physicians to make personalized, data-driven recommendations for treating chronic pain.

Clifton Sara M., Kang Chaeryon, Li Jingyi Jessica, Long Qi, Shah Nirmish, and Abrams Daniel M. Hybrid Statistical and Mechanistic Mathematical Model Guides Mobile Health Intervention for Chronic Pain.  Journal of Computational Biology. July 2017, 24(7): 675-688.

Photo by Artem Sapegin on Unsplash

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