Abstract: Though the potential impact of machine learning in healthcare warrants genuine enthusiasm, the increasing computerization of the field is still often seen as a negative rather than a positive. The limited adoption of machine learning in healthcare to date highlights the fact that there remain important challenges. In this talk, I will highlight two key challenges related to applying machine learning in healthcare: 1) interpretability and 2) small sample size. First, machine learning has often been criticized for producing ‘black boxes.’ In this talk, I will argue that interpretability is neither necessary nor sufficient, demonstrating that even interpretable models can lack common sense. To address this issue, we propose a novel regularization method that enables the incorporation of domain knowledge during model training, leading to increased robustness. Second, machine learning techniques benefit from large amounts of data. However, oftentimes in healthcare we find ourselves in data poor settings (i.e., small sample sizes). I will show how domain knowledge can help guide architecture choices and efficiently make use of available data. There’s a critical need for machine learning in healthcare; however, the safe and meaningful adoption of these techniques requires close collaboration in interdisciplinary teams and a careful understanding of one’s domain.

Jenna Wiens is a Morris Wellman 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 and transfer/multitask learning. 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. In 2015 she was named Forbes 30 under 30 in Science and Healthcare; she received an NSF CAREER Award in 2016; and recently she was named to the MIT Tech Review's list of Innovators Under 35.