Neural Pharmacodynamic State Space Modeling
Zeshan M Hussain, Rahul G. Krishnan, David Sontag
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.
BibTeX
@InProceedings{pmlr-v139-hussain21a,
title = {Neural Pharmacodynamic State Space Modeling},
author = {Hussain, Zeshan M and Krishnan, Rahul G. and Sontag, David},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {4500--4510},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/hussain21a/hussain21a.pdf},
url = {https://proceedings.mlr.press/v139/hussain21a.html},
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.}
}