Machine learning for non-myopic decision support and knowledge discovery

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The goal of our proposed research is to develop methods for using data to help humans make good long-term decisions. As a motivating example, consider choosing a medical treatment for a patient with a chronic disorder: in practice, treatment decisions are made based on knowledge about the state of the patient, knowledge about the treatment options currently available, and knowledge about how future decisions will be influenced by the patient's progress over time. Another example is the control of a water reservoir: decisions about how much water to use to generate power or irrigate crops are made based on knowledge about current demand, the state of the reservoir, and about the potential useage decisions that might be made in the future. We say a decision is "non-myopic" if it is made based on knowledge of the potential for future decision-making. In both of these examples, ultimately the final decision rests in human hands; however, there is enormous potential for that decision to be guided by sources of sequential data that are becoming more and more ubiquitous. For example, we are now seeing the development of databases that record how thousands or even millions of patients respond to different sequences of treatments over time, and these have the potential to inform non-myopic medical decision making more effectively than previous studies. However, rigorous analysis methods for constructing non-myopic decision aids from data are still in their infancy. Analysis methods in reinforcement learning and machine learning have enormous potential, but in many ways are not suited to decision aid construction: current methods do not effectively account for user preferences, they do not provide appropriate measures of confidence in their recommendations, and they do not account for the cost of gathering new data. My research aims to develop new methods without these shortcomings that can be applied to produce useful decision aids from sequential data. In the long term, as the depth and breadth of sequential medical data increases, the methods will improve the delivery of health care in Canada by providing our medical doctors with new, high-quality evidence to aid them in choosing the best treatments for their patients.