# Nonstationary Multivariate Gaussian Processes for Electronic Health Records

arXiv: https://arxiv.org/abs/1910.05851 · HTML: https://mlsanity.com/paper/1910.05851/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Multivariate Probabilistic Time Series Forecasting with Correlated Errors](https://mlsanity.com/paper/2402.01000/) (2024)
- [Modeling massive highly-multivariate nonstationary spatial data with the basis graphical lasso](https://mlsanity.com/paper/2101.02404/) (2021)
- [Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model](https://mlsanity.com/paper/2106.13379/) (2021)
- [Unsupervised Probabilistic Models for Sequential Electronic Health Records](https://mlsanity.com/paper/2204.07292/) (2022)
- [Non-stationary and Sparsely-correlated Multi-output Gaussian Process with Spike-and-Slab Prior](https://mlsanity.com/paper/2409.03149/) (2024)
- [Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network](https://mlsanity.com/paper/2106.00719/) (2021)

## Abstract (arXiv)

We propose multivariate nonstationary Gaussian processes for jointly modeling multiple clinical variables, where the key parameters, length-scales, standard deviations and the correlations between the observed output, are all time dependent. We perform posterior inference via Hamiltonian Monte Carlo (HMC). We also provide methods for obtaining computationally efficient gradient-based maximum a posteriori (MAP) estimates. We validate our model on synthetic data as well as on electronic health records (EHR) data from Kaiser Permanente (KP). We show that the proposed model provides better predictive performance over a stationary model as well as uncovers interesting latent correlation processes across vitals which are potentially predictive of patient risk.