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A0657
Title: Statistical and dynamical systems modeling of m-intervention for pain Authors:  Chae Ryon Kang - University of Pittsburgh (United States) [presenting]
Daniel Abrams - Northwestern University (United States)
Jingyi Li - University of California Los Angeles (United States)
Qi Long - University of Pennsylvania (United States)
Nirmish Shah - Duke University (United States)
Abstract: With the growing popularity of mobile phone technology, new opportunities have arisen for real-time adaptive medical intervention. The simultaneous growth of multiple big data sources (eg. mobile health data, electronic health records, lab test results, genomic data) allows for the development of personalized recommendations. We develop a new mathematical model for changes in subjective pain over time in patients with chronic conditions. The proposed model consists of a dynamical systems approach using differential equations to forecast future pain levels, as well as a statistical approach tying system parameters to patient data (including reported pain levels, medication history, personal characteristics and other health records). The model is combined with statistical techniques to ultimately obtain optimized, continuously-updated treatment plans balancing competing demands of pain reduction and medication minimization. Application of the resulting personalized treatment plans to a currently active pilot study on mobile intervention in patients living with chronic pain due to sickle cell disease (SCD) will be presented.