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Activity Number: 421
Type: Topic Contributed
Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #308534
Title: Statistics Aids in Development of Personalized Modules to Improve Medication Adherence
Author(s): Yan Wang*+ and Asya Spears and Honghu Liu
Companies: Fielding School of Public Health, UCLA and Fielding School of Public Health, UCLA and School of Dentistry, UCLA
Keywords: Adherence ; Real-time ; HMMs ; Customized healthcare ; Python
Abstract:

Medication adherence refers to whether patients take medications as scheduled and whether they continue to take the medication. The excellent medication adherence is critical for certain diseases, e.g. HIV. Traditional longitudinal data analysis for adherence behavior is always limited to few waves of measurements and delayed analysis of the events. The complexity and variety of individual behavior often needs to move beyond simple time-graded effects and needs personalized healthcare settings. Real-time intensive longitudinal data arise in the situation where the characteristics of individuals are recorded intensively and analyzed as soon as the events occur and therefore possible for real-time personalized action. Modern technologies assist us to monitor and collect real-time data on adherence, such as wisepill device. We use Hidden Markov Models to predict the probability of recent future missing dosage, in order to have intelligent intervention and customized healthcare, such as generating personalized reminder text messages to cell phone and making contingency therapy appointment in the coming week. The device is able to aid personalized adherence improvement in real-time.


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