Neural Pharmacodynamic State Space Modeling

2021/02/22 by Zeshan Hussain, Rahul G. Krishnan, Hussain, Zeshan +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #Cancer Genomics and Diagnostics #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare

paper · pdf · doi:10.48550/arxiv.2102.11218

openalex publication_date 2021/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Adam: A Method for Stochastic Optimization
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Hierarchical Mixtures of Experts and the EM Algorithm
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• Incorporating Symmetry into Deep Dynamics Models for Improved Generalization

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• Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs

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