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Neural Pharmacodynamic State Space Modeling
Zeshan Hussain · Rahul G. Krishnan · David Sontag

Tue Jul 20 07:40 PM -- 07:45 PM (PDT) @
in Optimization 4 »

Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression. However, existing neural network based approaches that learn representations of patient state, while very flexible, are susceptible to overfitting. We propose a deep generative model that makes use of a novel attention-based neural architecture inspired by the physics of how treatments affect disease state. The result is a scalable and accurate model of high-dimensional patient biomarkers as they vary over time. Our proposed model yields significant improvements in generalization and, on real-world clinical data, provides interpretable insights into the dynamics of cancer progression.

Keywords: Healthcare · Non-convex Optimization · Sparsity and Compressed Sensing · Algorithms

Author Information

Zeshan Hussain (MIT)
Rahul G. Krishnan (Microsoft Research)
David Sontag (Massachusetts Institute of Technology)

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