David Kale from the USC Computer Science Department moderated the session “Machine Learning in Complex Medical Data”. Medical Data exemplifies the “5 V’s” of Big Data: volume, velocity, veracity, variety, and value. The session brought three expert panelists to address how complex medical data can be used to better understand the health care system. These speakers included Drake Pruitt of Lionsolver, Adam Perer from IBM, and Finale Doshi-Velez from Harvard Medical School.
Finale Doshi-Velez is an NSF postdoctoral fellow at the Center for Biomedical Informatics at Harvard Medical School, where her research focuses on developing machine learning techniques to extract patterns in clinical data. She completed her doctoral work at the Massachusetts Institute of Technology. Her research focuses on data-driven approaches to discovering disease subtypes by applying latent variable analyses to clinical data. She also works on predictive models with time-series data.