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

Conference Paper

Nearly a quarter of visits to the emergency department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. 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. SCD is a chronic blood disorder in which pain is the most frequent complication. There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. Facing these challenges, in this study, we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. We present a new hybrid model for the dynamics of subjective pain that consists of a dynamical systems approach using differential equations to predict future pain levels, as well as a statistical approach tying system parameters to patient data (both personal characteristics and medication response history). 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.

Full Text

Duke Authors

Cited Authors

  • Clifton, SM; Kang, C; Li, JJ; Long, Q; Shah, N; Abrams, DM

Published Date

  • July 2017

Published In

Volume / Issue

  • 24 / 7

Start / End Page

  • 675 - 688

PubMed ID

  • 28581814

Pubmed Central ID

  • PMC5510708

Electronic International Standard Serial Number (EISSN)

  • 1557-8666

Digital Object Identifier (DOI)

  • 10.1089/cmb.2017.0059

Conference Location

  • United States