# Unsupervised Probabilistic Models for Sequential Electronic Health Records

arXiv: https://arxiv.org/abs/2204.07292 · HTML: https://mlsanity.com/paper/2204.07292/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes](https://mlsanity.com/paper/1704.02801/) (2017)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier](https://mlsanity.com/paper/1706.04152/) (2017)
- [Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes](https://mlsanity.com/paper/1610.08853/) (2016)
- [Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records](https://mlsanity.com/paper/2003.10170/) (2020)
- [Nonstationary Multivariate Gaussian Processes for Electronic Health Records](https://mlsanity.com/paper/1910.05851/) (2019)
- [Methodological variations in lagged regression for detecting physiologic drug effects in EHR data](https://mlsanity.com/paper/1801.08929/) (2018)

## Built on by (most cited first, 1 of 1)

- [Sequential Inference of Hospitalization Electronic Health Records Using Probabilistic Models](https://mlsanity.com/paper/2403.19011/) (2024)

## Abstract (arXiv)

We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for subgrouping and incorporation of the dynamics underlying heterogeneous data types. The model consists of a layered set of latent variables that encode underlying structure in the data. These variables represent subject subgroups at the top layer, and unobserved states for sequences in the second layer. We train this model on episodic data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The resulting properties of the trained model generate novel insight from these complex and multifaceted data. In addition, we show how the model can be used to analyze sequences that contribute to assessment of mortality likelihood.