21 August, 2017The Evolving Role of Machine Learning in Healthcare
Dr. Michael D. Kuo, MD, from Department of Radiological Sciences of University of California, Los Angeles (UCLA), gave a talk examining the role and challenges of data science, in particular machine learning, in healthcare and bioengineering arena. The talk was jointly held by the Department of Chemical and Biological Engineering and the HKUST Big Data Institute.
Medicine, by definition, is an information science that requires the capacity to actively acquire individualized and context-specific data, and to then iteratively evaluate, assimilate and refine this information against a vast database of medical knowledge in order to arrive at a small solution space with a corresponding set of individually tailored implementable policies. Machine Learning (ML), as a transformational tool, is thus potentially extremely well suited to medical application. Unfortunately, while technological advances in ML are being made at a rapid pace in other domains, impactful medical application significantly. In this talk, Dr. Kuo discussed problems currently being addressed with machine learning, how his group and others are beginning to incorporate it across a number of different facets in medicine, highlight areas of future growth and opportunity, as well as areas of existing limitation or future ethical concern.
Dr. Kuo received his Medical Degree from Baylor College of Medicine and did his clinical training in Diagnostic Radiology at Stanford University, where he also completed a clinical fellowship in Cardiovascular and Interventional Radiology. He served as Assistant Professor in the Department of Radiology at the University of California-San Diego from 2003-2009. In 2009, he moved to the University of California-Los Angeles where he is an Associate Professor in the Departments of Radiology, Pathology and Bioengineering and served as the Directors of both the Radiogenomics and Radiology-Pathology Programs. Dr. Kuo is an international leader in the field of Radiogenomics where he has published seminal foundational papers. His principle area of research focus is in the field of radiogenomics where his group applies integrative computational and biological approaches in order to derive actionable clinical insights and tools centered around patient stratification and therapeutic response prediction by leveraging large multi-scale relational data sets including clinical outcomes, clinical imaging, tissue, cellular and subcellular biological data.