# Modeling Irregularly Sampled Clinical Time Series

arXiv: https://arxiv.org/abs/1812.00531 · HTML: https://mlsanity.com/paper/1812.00531/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units](https://mlsanity.com/paper/2102.04702/) (2021)
- [SDE-Attention: Latent Attention in SDE-RNNs for Irregularly Sampled Time Series with Missing Data](https://mlsanity.com/paper/2511.23238/) (2025)
- [DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification](https://mlsanity.com/paper/2604.11842/) (2026)
- [Deep Kernel Learning for Stratifying Glaucoma Trajectories](https://mlsanity.com/paper/2605.00708/) (2026)
- [CT-HEG: A Bidirectional, Timestamp-Attributed Event Graph for ICU In-Hospital Mortality Prediction - An Architectural Ablation Study](https://mlsanity.com/paper/2608.02663/) (2026)

## Abstract (arXiv)

While the volume of electronic health records (EHR) data continues to grow, it remains rare for hospital systems to capture dense physiological data streams, even in the data-rich intensive care unit setting. Instead, typical EHR records consist of sparse and irregularly observed multivariate time series, which are well understood to present particularly challenging problems for machine learning methods. In this paper, we present a new deep learning architecture for addressing this problem based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions during the interpolation stage, while any standard deep learning model can be used for the prediction network. We investigate the performance of this architecture on the problems of mortality and length of stay prediction.