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Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time
December 1, 2017 @ 11:00 am - 12:00 pm
Susan Murphy (Harvard)
Abstract: A formidable challenge in designing sequential treatments is to determine when and in which context it is best to deliver treatments. Consider treatment for individuals struggling with chronic health conditions. Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment. That is, the treatment is adapted to the individual’s context; the context may include current health status, current level of social support and current level of adherence for example. Data sets on individuals with records of time-varying context and treatment delivery can be used to inform the construction of the decision rules. There is much interest in personalizing the decision rules, particularly in real time as the individual experiences sequences of treatment. Here we discuss our work in designing online “bandit” learning algorithms for use in personalizing mobile health interventions.
Biography: Susan A. Murphy is Professor of Statistics, Radcliffe Alumnae Professor at the Radcliffe Institute and Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences, all at Harvard University. Her lab focuses on improving sequential, individualized, decision making in health, in particular on clinical trial design and data analysis to inform the development of just-in-time adaptive interventions in mobile health. The lab’s work is funded by National Institute on Drug Abuse , by National Institute on Alcohol Abuse and Alcoholism, by National Heart, Lung and Blood Institute and by National Institute of Biomedical Imaging and Bioengineering. Susan is a Fellow of the Institute of Mathematical Statistics, a Fellow of the College on Problems in Drug Dependence, a former editor of the Annals of Statistics, a member of the US National Academy of Sciences, a member of the US National Academy of Medicine and a 2013 MacArthur Fellow.