# A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data

arXiv: https://arxiv.org/abs/2010.12493 · HTML: https://mlsanity.com/paper/2010.12493/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 30 of 31)

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- [IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers](https://mlsanity.com/paper/2305.06741/) (2023)
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- [MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records](https://mlsanity.com/paper/2407.00840/) (2024)
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- [COPER: Continuous Patient State Perceiver](https://mlsanity.com/paper/2208.03196/) (2022)
- [Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement using Electronic Health Records](https://mlsanity.com/paper/2409.06585/) (2024)
- [Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis](https://mlsanity.com/paper/2412.16098/) (2024)
- [Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network](https://mlsanity.com/paper/2102.01147/) (2021)
- [Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series](https://mlsanity.com/paper/2311.07744/) (2023)
- [Individualized Dynamic Latent Factor Model for Multi-resolutional Data with Application to Mobile Health](https://mlsanity.com/paper/2311.12392/) (2023)
- [Curricular and Cyclical Loss for Time Series Learning Strategy](https://mlsanity.com/paper/2312.15853/) (2023)
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- [Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples](https://mlsanity.com/paper/2402.02081/) (2024)
- [Review of Data-centric Time Series Analysis from Sample, Feature, and Period](https://mlsanity.com/paper/2404.16886/) (2024)
- [Incorporating Taylor Series and Recursive Structure in Neural Networks for Time Series Prediction](https://mlsanity.com/paper/2402.06441/) (2024)
- [Evaluating Time Series Models with Knowledge Discovery](https://mlsanity.com/paper/2503.14869/) (2025)
- [Closing Gaps: An Imputation Analysis of ICU Vital Signs](https://mlsanity.com/paper/2510.24217/) (2025)
- [VitalBench: A Rigorous Multi-Center Benchmark for Long-Term Vital Sign Prediction in Intraoperative Care](https://mlsanity.com/paper/2511.13757/) (2025)
- [Aortic Valve Disease Screening from PPG via Physiology-Guided Self-Supervised Learning](https://mlsanity.com/paper/2602.04266/) (2026)
- [DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification](https://mlsanity.com/paper/2604.11842/) (2026)
- [Interpretable Machine Learning for Antepartum Prediction of Pregnancy-Associated Thrombotic Microangiopathy Using Routine Longitudinal Laboratory Data](https://mlsanity.com/paper/2605.13786/) (2026)
- [MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification](https://mlsanity.com/paper/2605.24961/) (2026)
- [TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs](https://mlsanity.com/paper/2606.09030/) (2026)

## Abstract (arXiv)

Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences. ISTS commonly appears in healthcare, economics, and geoscience. Especially in the medical environment, the widely used Electronic Health Records (EHRs) have abundant typical irregularly sampled medical time series (ISMTS) data. Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management. However, it is challenging to directly use deep learning models for ISMTS data. On the one hand, ISMTS data has the intra-series and inter-series relations. Both the local and global structures should be considered. On the other hand, methods should consider the trade-off between task accuracy and model complexity and remain generality and interpretability. So far, many existing works have tried to solve the above problems and have achieved good results. In this paper, we review these deep learning methods from the perspectives of technology and task. Under the technology-driven perspective, we summarize them into two categories - missing data-based methods and raw data-based methods. Under the task-driven perspective, we also summarize them into two categories - data imputation-oriented and downstream task-oriented. For each of them, we point out their advantages and disadvantages. Moreover, we implement some representative methods and compare them on four medical datasets with two tasks. Finally, we discuss the challenges and opportunities in this area.