Session Chair Profile
Ph.D., Executive Director, Institution for Computational Health Sciences, UCSF
Sharat Israni is Executive Director of UCSF’s Institute for Computational Health Sciences, which is building UCSF’s next-gen research computing capability. Previously, he was Executive Director, Data Science, at Stanford Medicine (Dean’s Office). A long-serving Technology executive, Sharat’s teams pioneered the use of “Big Data.” He served as VP of Data at both Yahoo! (1999-2008) and Intuit (2010-13), as these companies pioneered “Big” Data Science to re-invent their products. He led Digital Media systems for broadcast/interactive TV at Silicon Graphics; and Data Systems teams at IBM/Metaphor and HP. Sharat has organized workshops for NSF, NIH and RCUK on today’s topic.
Clinical Dx Showcase: Big Data in Precision Medicine – Lessons from the Tech Industry
Over the last five years, interest in “data” has grown strongly in medical research and practice. However, the systems and applications emerging look like they could gain much from industry experience over the last 20 years in truly “Big” Data, employing deep computer science and data mining at massive scale. We present our research computing capability for Precision Medicine that draws on this experience. Instructive points of light are emerging, with real application benefits, that we present in non-technical parlance comfortable to the medical professional. For instance, data storage with a profile of “Compute to the Data” that makes genetic or clinical patterns feasibly discoverable. Graph-theoretic structures reflecting the very nature of human biomedical pathways. NLP for clinical text that improves with true machine learning. Google-search style expansion of entity recognition beyond standard medical ontologies. Adoption of voice recognition to bring alive new data. Research versus clinical practice quality and service levels, that will actually make Big Data practical for medicine. These applications exhibit a depth of computational and inferential thinking, reflecting engineering and math sciences. Above all, the mindset employed by the tech industry can truly guide Precision Medicine, and even make the difference between success and failure.