# Neural Pharmacodynamic State Space Modeling

arXiv: https://arxiv.org/abs/2102.11218 · HTML: https://mlsanity.com/paper/2102.11218/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [Cormorant: Covariant Molecular Neural Networks](https://mlsanity.com/paper/1906.04015/) (2019)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [Structured Inference Networks for Nonlinear State Space Models](https://mlsanity.com/paper/1609.09869/) (2016)
- [Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations](https://mlsanity.com/paper/2002.04083/) (2020)
- [Incorporating Symmetry into Deep Dynamics Models for Improved Generalization](https://mlsanity.com/paper/2002.03061/) (2020)
- [A Clockwork RNN](https://mlsanity.com/paper/1402.3511/) (2014)
- [Differentiable Physics-informed Graph Networks](https://mlsanity.com/paper/1902.02950/) (2019)
- [Observational-Interventional Priors for Dose-Response Learning](https://mlsanity.com/paper/1605.01573/) (2016)
- [DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders](https://mlsanity.com/paper/1901.03553/) (2019)
- [Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions](https://mlsanity.com/paper/1704.02038/) (2017)

## Built on by (most cited first, 12 of 12)

- [Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression](https://mlsanity.com/paper/2106.02875/) (2021)
- [Structured Neural Networks for Density Estimation and Causal Inference](https://mlsanity.com/paper/2311.02221/) (2023)
- [Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Response](https://mlsanity.com/paper/2402.17233/) (2024)
- [Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models](https://mlsanity.com/paper/2110.14993/) (2021)
- [Entity Aware Modelling: A Survey](https://mlsanity.com/paper/2302.08406/) (2023)
- [AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires](https://mlsanity.com/paper/2304.13737/) (2023)
- [Global Deep Forecasting with Patient-Specific Pharmacokinetics](https://mlsanity.com/paper/2309.13135/) (2023)
- [Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction](https://mlsanity.com/paper/2204.13451/) (2022)
- [Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild](https://mlsanity.com/paper/2312.03344/) (2023)
- [Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs](https://mlsanity.com/paper/2505.18996/) (2025)
- [HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning](https://mlsanity.com/paper/2602.22630/) (2026)
- [Constrained latent state modeling: A unifying perspective on representation learning under competing constraints](https://mlsanity.com/paper/2605.15995/) (2026)

## Abstract (arXiv)

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.