Jenna Wiens Research Group

University of Michigan-Ann Arbor

How can hospital data support useful clinical decisions when measurements are incomplete and model predictions can be unreliable? The group develops machine-learning approaches using patient images, laboratory tests and vital signs. Research includes predicting infections and other patient outcomes, learning treatment policies with causal inference and offline reinforcement learning, and evaluating how clinicians respond to AI recommendations. Another direction investigates selective prediction: withholding potentially unreliable outputs to reduce automation bias. The work connects algorithm design with the practical effects of deploying models in clinical workflows.