Speaker: Elizabeth S. Burnside, MD PhD
Department of Radiology; Department of Biostatistics and Medical Informatics
University of Wisconsin School of Medicine and Public Health

Using Multi-relational Data and Machine Learning to Improve Breast Cancer Diagnosis
Tuesday
4:00 pm
Biotechnology Center Auditorium, 425 Henry Mall
Abstract:
    In the new era of "-omic"-based research, many scientists have shifted from the study of the individual parts of a system to the system itself. This new paradigm focuses on a comprehensive collection of a fundamental data type that can provide a platform for a myriad of research directions on a given level ranging from the subcellular to the population. However, developing methodologies that integrate these rich data sources to inform and improve healthcare decisions on the patient level remains challenging. Our team of physicians, computer scientists, and industrial engineers at the University of Wisconsin has collaborated for the last decade to develop methods to improve breast cancer diagnostic decision-making using inductive logic programming, statistical relational learning, advice-based-learning, and other cutting-edge machine learning techniques. Our algorithms are designed to utilize the ever-expanding, multi-relational data that predicts breast cancer including: genetic, imaging, and epidemiologic risk factors. This talk will present an overview of our research programs and provide a vision of the future of computational methods in the domain of breast cancer risk prediction.