Speaker: Kimberly Robasky PhD
Presented from UNC-CH
Broadcast Link: Seminar
Some estimates place the cost of bringing a drug to market at one billion U.S dollars and hence reducing the cost and length of clinical trials can indirectly lower healthcare costs. One source of clinical trial failure, lack of efficacy and safety, could be mitigated through decision support for patient stratification. As part of the NIH-funded Biomedical Data Translator project, we are integrating multiple, previously disparate datasets, which is empowering investigators with new tools for data-driving patient subtyping. For example, through the Data Translator project, we can combine clinical records with exposure data in support of powerful models for classification. We have implemented supervised and unsupervised machine learning models on these data for predicting patient outcomes according to exposure in order to better understand patient disease and response.
Dr. Robasky earned her Ph.D. from Boston University on fellowship From George Church with Harvard’s Department of Genetics. She has 10+ years of experience in architecting and delivering sustainable software systems, and spent several years as a scientist and product developer for a sequencing lab owned by Quintiles. Dr. Robasky joined the Renaissance Computing Institute (RENCI) in the fall of 2016 and holds adjuncts to both the Department of Genetics and the School of Information and Library Science at the University of North Carolina at Chapel Hill. Dr. Robasky is a contributor to the NIH Biomedical Data Translator and NIH Data Commons Pilot programs.