Electrical Engineering and Computer Science


AI Seminar

Set-Valued Dynamic Treatment Regimes for Competing Outcomes

Dan Lizotte


Assistant Professor, Department of Computer Science and Department of Epidemiology & Biostatistics
University of Western Ontario
 
Tuesday, February 10, 2015
4:00pm - 5:30pm
3725 BBB

Add to Google Calendar

About the Event

Dynamic treatment regimes ("policies" in the reinforcement learning literature) operationalize clinical decision-making as a sequence of functions, one for each clinical decision, where each function maps patient features to a recommended treatment. Reinforcement learning (RL) methods for learning optimal dynamic treatment regimes, for example Q-learning, require the specification of a single outcome or "reward" that measures the quality of the decisions. However, in practice clinical decision making aims to balance several potentially competing outcomes, e.g., symptom relief and side-effect burden. When there are competing outcomes and patients do not know or cannot communicate the relative importance of each of them, forming a single reward that captures "optimal decision-making" is not possible. I will discuss recent developments in RL for learning dynamic treatment regimes that accommodate competing outcomes by recommending sets of treatments at each decision point. The methods will be illustrated using data from the CATIE schizophrenia study.

Biography

Professor Lizotte is interested in the areas of machine learning, reinforcement learning, and statistics, particularly as they apply to problems in health informatics. We are now seeing the development of electronic data sources that record how thousands or even millions of patients respond to different sequences of treatments over time, and these have the potential to inform evidence-based non-myopic medical decision making more effectively than previous studies. However current techniques are not always well-suited to this task. Professor Lizotte's basic research aims to adapt and improve reinforcement learning, machine learning, and statistical techniques so they can be applied to these new sources of sequential medical data, and can in turn provide doctors with the best available evidence for non-myopic decision making.

Additional Information

Sponsor(s): Toyota

Open to: Public