mlsanityData to 7 Oct 2026

Improving Clinical Predictions through Unsupervised Time Series Representation Learning

arXiv 1812.00490 ↗First posted 2 Dec 2018cs.LG stat.MLcited by 24

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Abstract (arXiv)

In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order to eventually assist clinical decision making. By evaluating on the prediction of clinically relevant outcomes, we show that in a practical setting, unsupervised representation learning can offer clear performance benefits over end-to-end supervised architectures. We experiment with using sequence-to-sequence (Seq2Seq) models in two different ways, as an autoencoder and as a forecaster, and show that the best performance is achieved by a forecasting Seq2Seq model with an integrated attention mechanism, proposed here for the first time in the setting of unsupervised learning for medical time series.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 6; 2023: 5; 2024: 1; 2025: 5; 2026: 1.

24 papers.

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  1. 2021Unsupervised Representation Learning for Time Series with Temporal Neighborhood Codingcited by 223
  2. 2022Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusioncited by 96
  3. 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Reviewcited by 35
  4. 2021Neighborhood Contrastive Learning Applied to Online Patient Monitoringcited by 32
  5. 2022MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Seriescited by 28
  6. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
  7. 2023Generative modeling for time series via Schr"odinger bridgecited by 18
  8. 2023Unsupervised Representation Learning for Time Series: A Reviewcited by 14
  9. 2022On the Importance of Clinical Notes in Multi-modal Learning for EHR Datacited by 10
  10. 2025Deep Learning for Time Series Forecasting: A Surveycited by 10
  11. 2023Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encodercited by 5
  12. 2021Conditional Loss and Deep Euler Scheme for Time Series Generationcited by 4
12 more
  1. 2021A Self-Supervised Framework for Function Learning and Extrapolationcited by 2
  2. 2022Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomaxcited by 2
  3. 2022Convolution-enhanced Evolving Attention Networkscited by 2
  4. 2021Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learningcited by 1
  5. 2022Deep Normed Embeddings for Patient Representationcited by 1
  6. 2023Fully Embedded Time-Series Generative Adversarial Networkscited by 1
  7. 2024Learning to Select the Best Forecasting Tasks for Clinical Outcome Predictioncited by 0
  8. 2025Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementiacited by 0
  9. 2025Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Modelcited by 0
  10. 2025Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learningcited by 0
  11. 2025MTS-DMAE: Dual-Masked Autoencoder for Unsupervised Multivariate Time Series Representation Learningcited by 0
  12. 2026SMart: A Multi-source Multi-phase Time Series Representation Transfer Frameworkcited by 0