@inproceedings{hussain2021neural,
author = {Hussain, Zeshan and Gopal Krishnan, Rahul and Sontag, David},
title = {Neural Pharmacodynamic State Space Modeling},
booktitle = {2021 International Conference on Machine Learning},
year = {2021},
month = {July},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/neural-pharmacodynamic-state-space-modeling/},
}