Representation, Structure, and Context: Developing Computational Frameworks for Real-World Health Data
The growing scale and scope of real-world health data offer new opportunities to advance our understanding of human health, yet their availability alone cannot ensure effective use. Data generated through healthcare are noisy, high-dimensional, and heterogeneous, while diverse collection practices often leave only a fragmented view of an individual and their care. In practice, these data are frequently reduced to collections of discrete features, obscuring relationships across medical histories and uncertainty when information is incomplete. This talk explores the development of computational approaches that more fully account for this complexity by capturing comprehensive and contextualized representations of the available information. In doing so, this work moves beyond simply adding more features or increasing model complexity to improve how machine-learning and statistical models learn from messy, real-world data and reflect variability across patient conditions, their care, and downstream outcomes.
Biography
Dr. Keith Feldman is an Associate Professor in the Department of Learning Health Sciences and Associate Professor of Pediatrics at the University of Michigan Medical School. A computer scientist by training, his academic interests lie at the intersection of computational methods and healthcare, with an emphasis on neonatal and pediatric care. His research program centers on advancing how machine-learning models learn from complex, real-world data. This includes a body of methodological research developing more comprehensive ways to model real-world systems. A complementary translational arm has produced evidence-based risk measures that explore variability between patient conditions and outcomes. Across this work, his goal is to augment existing processes with information and context rather than automate them. His work has been supported by the NIH, the American Heart Association, the Frontiers and MICHR CTSIs, and generous philanthropic gifts.
