Towards Patient-Specific Treatment: Medical Applications of Machine Learning

Speaker:

Russ Greiner,

Professor, University of Alberta, Computing Science

Founding Scientific Director, Alberta Machine Intelligence Institute (AMII)

CIFAR AI Chair

Bio:

After earning a PhD from Stanford, Russ Greiner worked in both academic and industrial research before settling at the University of Alberta, where he is now a Professor in Computing Science and the founding Scientific Director of the Alberta Machine Intelligence Institute. He has been Program/Conference Chair for various major conferences, and has served on the editorial boards of a number of other journals. He was elected a Fellow of the AAAI (Association for the Advancement of Artificial Intelligence), was awarded a McCalla Professorship and a Killam Annual Professorship; received a 2020 FGSR Great Supervisor Award and in 2021, received the CAIAC Lifetime Achievement Award and became a CIFAR AI Chair. He has published over 300 refereed papers, most in the areas of machine learning and recently medical informatics, including 5 that have been awarded Best Paper prizes. The main foci of his current work are (1) bio- and medical- informatics; (2) learning and using effective probabilistic models and (3) formal foundations of learnability. 

Abstract:

Patient-specific treatment requires determining which treatment has the best chance of success for an individual patient, based on all available information. As this typically depends on many patient characteristics, finding a single biomarker is not sufficient; nor is it enough to find the set of top biomarkers, as the best treatment depends on how multiple factors collectively relate to the outcome, perhaps combined using a classifier such as a decision tree. In many situations, these "best treatment" classifiers are not known initially. Fortunately, there is often a corpus of historical data, which includes both descriptions of previous patients, as well as the treatment outcomes. The field of Machine Learning (ML) provides tools to help here -- tools that can "learn" which treatment is most effective for a given patient, based on his/her specific symptoms.

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