5:00 - 5:30 (30 min)
Improving Patient Care through Data
The increase in the availability of clinically relevant datasets has led to the investigation of machine learning (ML) techniques for improving clinical decision making. However, for data-driven systems to become widely and safely adopted in clinical care, there remain several key research challenges that the ML community must address. In particular, many existing risk stratification models while accurate are not necessarily actionable. We should be focusing on building models that produce both accurate and actionable predictions. In this talk, I’ll present/motivate this challenge in the context of building risk stratification models to predict adverse events during a hospital stay, and will discuss new and ongoing research directions in ML that aim to tackle this issue.
Jenna Wiens is an Assistant Professor of Computer Science and Engineering (CSE) at the University of Michigan in Ann Arbor. Her primary research interests lie at the intersection of machine learning, data mining, and healthcare. She is particularly interested in time-series analysis, transfer/multitask learning and causal inference. The overarching goal of her research agenda is to develop the computational methods needed to help organize, process, and transform patient data into actionable knowledge. Jenna received her PhD from MIT in 2014. Recently, she received an NSF CAREER Award, and in 2015 was named Forbes 30 under 30 in Science and Healthcare.