David Sprott Distinguished Lecture by Susan Murphy, University of MichiganExport this event to calendar

Thursday, September 28, 2017 — 4:00 PM EDT

Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time


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. 


Susan MurphySusan A. Murphy, currently at the University of Michigan will be joining Harvard University in September 2017. Her research 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.    She 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, the US National Academy of Medicine and a 2013 MacArthur Fellow.

Location 
MC - Mathematics & Computer Building
Room: 4021
200 University Avenue West
Waterloo, ON N2L 3G1
Canada

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