Neural Pharmacodynamic State Space Modeling

• URL: http://arxiv.org/abs/2102.11218v1
• Date: Mon, 22 Feb 2021 17:51:11 GMT
• Title: Neural Pharmacodynamic State Space Modeling
• Authors: Zeshan Hussain, Rahul G. Krishnan, David Sontag
• Abstract summary: 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. Our proposed model yields significant improvements in generalization and, on real-world clinical data, provides interpretable insights into the dynamics of cancer progression.
• Score: 1.589915930948668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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