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A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure
Peter Schulam · Suchi Saria

Wed Dec 09 04:00 PM -- 08:59 PM (PST) @ 210 C #21 #None

For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual's disease, which can in turn enable clinicians to optimize treatments. We represent an individual's disease trajectory as a continuous-valued continuous-time function describing the severity of the disease over time. We propose a hierarchical latent variable model that individualizes predictions of disease trajectories. This model shares statistical strength across observations at different resolutions--the population, subpopulation and the individual level. We describe an algorithm for learning population and subpopulation parameters offline, and an online procedure for dynamically learning individual-specific parameters. Finally, we validate our model on the task of predicting the course of interstitial lung disease, a leading cause of death among patients with the autoimmune disease scleroderma. We compare our approach against state-of-the-art and demonstrate significant improvements in predictive accuracy.

Author Information

Peter Schulam (Johns Hopkins University)

Peter Schulam is a PhD student in computer science at Johns Hopkins University. His research interests include machine learning and its applications to healthcare. Peter has made methodological contributions to advancing the use of electronic health data for individualizing care in chronic diseases. His current work explores applications in autoimmune diseases. He has won the National Science Foundation (NSF) Graduate Research Fellowship and the Whiting School of Engineering Centennial Fellowship. He is working with Prof. Suchi Saria for his PhD. Prior to that, he received his master’s from Carnegie Mellon University and his bachelor’s from Princeton University.

Suchi Saria (Johns Hopkins University)

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