This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 595
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #308586
Title: Discovering Personalized Therapy for Cystic Fibrosis by Reinforcement Learning
Author(s): Yiyun Tang*+ and Michael Kosorok
Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Address: University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, US
Keywords: Personalized Therapy ; Cystic Fibrosis ; Reinforcement Learning ; Medical Decision Making ; Adaptive Treatment Strategy
Abstract:

Optimal personalized management of the inherited chronic diseases, such as cystic fibrosis,hold the promise of targeting medicines and dosages more precisely and safely for each patient. We propose to use reinforcement learning(RL) for discovering optimal personalized therapy, which bases on genetic biomarkers and time varying covariates from the longitudinal records,alters treatment decisions to achieve a favorable ultimate outcome.We conduct a simulation study of CF patients with Pseudomonas aeruginosa infection and antibiotic therapy,where a temporal difference RL approach is utilized to characterize effect of the treatment regimen in the course of chronic disease. Our results show the great capacity of reinforcement learning for discovering personalized therapy which optimise benefit-risk trade off in a multi-stage decision making to improve long term outcomes in chronic disease.


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