Online
Hosted by the Centre for Health Informatics in the Cumming School of Medicine at the University of Calgary, see Amii Fellow Dr. Russell Greiner present his talk "Towards Patient-Specific Treatment: Medical Applications of Machine Learning."
Patient-specific treatment requires determining which treatment has best chance of success for an individual patient, based on all available information. As this typically depends on many patient characteristics, the best treatment depends on how multiple factors collectively relate to the outcome. 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.
This presentation introduces the relevant ideas, using real-world medical examples -- starting with a way to help predict which breast cancer patients are likely to suffer a relapse, based on the subcellular location of certain adhesion proteins, as well as the standard clinical features. Dr. Greiner will show the difference between standard association studies (designed to find biomarkers) and this machine learning methodology. Next, he will demonstrate that this methodology can be used for a wide variety of other medical tasks.
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