# GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data

arXiv: https://arxiv.org/abs/2205.04892 · HTML: https://mlsanity.com/paper/2205.04892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 4 May 2022.

## Builds on (most cited first, 15 of 15)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://mlsanity.com/paper/1805.10572/) (2018)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series](https://mlsanity.com/paper/1905.12374/) (2019)
- [Attend and Diagnose: Clinical Time Series Analysis using Attention Models](https://mlsanity.com/paper/1711.03905/) (2017)
- [Qlib: An AI-oriented Quantitative Investment Platform](https://mlsanity.com/paper/2009.11189/) (2020)
- [RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data](https://mlsanity.com/paper/1807.08820/) (2018)
- [Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review](https://mlsanity.com/paper/2010.02809/) (2020)
- [DeepCare: A Deep Dynamic Memory Model for Predictive Medicine](https://mlsanity.com/paper/1602.00357/) (2016)
- [Discrete Event, Continuous Time RNNs](https://mlsanity.com/paper/1710.04110/) (2017)
- [Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU](https://mlsanity.com/paper/1806.02878/) (2018)
- [Early hospital mortality prediction using vital signals](https://mlsanity.com/paper/1803.06589/) (2018)
- [Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes](https://mlsanity.com/paper/1909.03039/) (2019)

## Abstract (arXiv)

Electronic health records (EHRs) are usually highly dimensional, heterogeneous, and multimodal. Besides, the random recording of clinical variables results in high missing rates and uneven time intervals between adjacent records in the multivariate clinical time-series data extracted from EHRs. Current works using clinical time-series data for patient representation regard the patients' physiological status as a discrete process described by sporadically collected records. However, changes in the patient's physiological condition are continuous and dynamic processes. The perception of time and velocity of change is crucial for patient representation learning. In this study, we propose a time- and velocity-aware gated recurrent unit model (GRU-TV) for patient representation learning of clinical multivariate time-series data in a time-continuous manner. The neural ordinary differential equations (ODEs) and velocity perception mechanism are applied to perceive the time interval between adjacent records and changing rate of the patient's physiological status, respectively. Our experiments on two real clinical EHR datasets (PhysioNet2012, MIMIC-III) establish that GRU-TV is a robust model on computer-aided diagnosis (CAD) tasks, especially on sequences with high-variance time intervals.