# A Bayesian Approach to Modelling Longitudinal Data in Electronic Health Records

arXiv: https://arxiv.org/abs/1912.09086 · HTML: https://mlsanity.com/paper/1912.09086/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

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

- [Improving Hospital Risk Prediction with Knowledge-Augmented Multimodal EHR Modeling](https://mlsanity.com/paper/2508.01970/) (2025)

## Abstract (arXiv)

Analyzing electronic health records (EHR) poses significant challenges because often few samples are available describing a patient's health and, when available, their information content is highly diverse. The problem we consider is how to integrate sparsely sampled longitudinal data, missing measurements informative of the underlying health status and fixed demographic information to produce estimated survival distributions updated through a patient's follow up. We propose a nonparametric probabilistic model that generates survival trajectories from an ensemble of Bayesian trees that learns variable interactions over time without specifying beforehand the longitudinal process. We show performance improvements on Primary Biliary Cirrhosis patient data.