Accuracy of Individualized Risk Estimates for Personalized Medicine

Lucila Ohno-Machado, Professor of Medicine and Chief of the Division of Biomedical Informatics at UC-San Diego, will be giving a talk on "Accuracy of Individualized Risk Estimates for Personalized Medicine" next week, August 18, noon-1pm in 202 Light Hall. This should be an interesting perspective from a scientist with medical training on the utility of personal genomics tools in making healthcare decisions.

Bio: Lucila Ohno-Machado, MD, PhD, is Professor of Medicine and founding chief of the Division of Biomedical Informatics at the University of California San Diego. She received her medical degree from the University of Sao Paulo and her doctoral degree in Medical Information Sciences from Stanford University. Prior to her current role, she was director of the training program for the Harvard-MIT-Tufts-Boston University consortium in Boston, and director of the Decision Systems Group at Brigham and Women's Hospital, Harvard Medical School. Her research focuses on the development of new evaluation methods for predictive models of disease, with special emphasis on the analysis of model calibration and implications in healthcare. She is an elected member of the American College of Medical Informatics, the American Institute for Medical and Biological Engineering, and the American Society for Clinical Investigation. She is associate editor for the Journal of the American Medical Informatics Association, and will become Editor-in-Chief in January 2011. Dr. Ohno-Machado will discuss the problems with evaluating individual risk estimates and predictive models based on binary outcomes using existing methods. She will present alternative methods for evaluating calibration of risk assessment tools and discuss implications in healthcare practice.

Abstract: Medical decision support tools are increasingly available on the Internet and are being used by lay persons as well as health care professionals. The goal of some of these tools is to provide an "individualized" prediction of future health care related events (e.g.,  prognosis of breast cancer given specific information about the individual). Under the umbrella of "personalized" medicine, these individualized prognostic assessments are sought as a means to replace general prognostic information with specific probability estimates that pertain to a small stratum to which the patient belongs, and ultimately specifically to each patient. Subsequently, these estimates are used to inform decision making and are therefore of critical importance for public health. In this presentation, I will discuss the problems with assessing the quality of individual estimates, present existing and proposed tools for evaluating prognostic models, and discuss implications for individual counseling.

This should be an interesting talk, and very relevant to current regulatory issues surrounding personal genomics.