mlsanityData to 7 Oct 2026

Neighborhood Contrastive Learning Applied to Online Patient Monitoring

arXiv 2106.05142 ↗First posted 9 Jun 2021cs.LGcited by 32

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Abstract (arXiv)

Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by supplementing time-series data augmentation techniques with a novel contrastive learning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 28 of its 43 references that are in the map. First posted on arXiv 9 Jun 2021.

Builds on

28 of 43 references.

References · 28

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  3. 2016Layer Normalizationcited by 5,579
  4. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  5. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  6. 2020Supervised Contrastive Learningcited by 2,803
  7. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  8. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  9. 2020Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hyperspherecited by 1,393
  10. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,375
  11. 2020What Makes for Good Views for Contrastive Learning?cited by 843
  12. 2019Self-Supervised Learning of Pretext-Invariant Representationscited by 806
16 more
  1. 2017Multitask learning and benchmarking with clinical time series datacited by 287
  2. 2019Unsupervised Scalable Representation Learning for Multivariate Time Seriescited by 267
  3. 2020CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patientscited by 170
  4. 2019Set Functions for Time Seriescited by 137
  5. 2017Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networkscited by 130
  6. 2020Uncovering the structure of clinical EEG signals with self-supervised learningcited by 109
  7. 2020Subject-Aware Contrastive Learning for Biosignalscited by 75
  8. 2020Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scalecited by 39
  9. 2018Data augmentation using synthetic data for time series classification with deep residual networkscited by 38
  10. 2020An Empirical Study of Representation Learning for Reinforcement Learning in Healthcarecited by 34
  11. 2018Adversarial Domain Adaptation for Stable Brain-Machine Interfacescited by 27
  12. 2018Improving Clinical Predictions through Unsupervised Time Series Representation Learningcited by 24
  13. 2020Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Datacited by 12
  14. 2020A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Datacited by 8
  15. 2020Real-time Prediction of COVID-19 related Mortality using Electronic Health Recordscited by 7
  16. 2019Unsupervised Representation for EHR Signals and Codes as Patient Status Vectorcited by 1

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Citing papers per year: 2021: 1; 2022: 5; 2023: 11; 2024: 12; 2025: 3.

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  1. 2023SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modelingcited by 112
  2. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  3. 2024PaPaGei: Open Foundation Models for Optical Physiological Signalscited by 64
  4. 2023Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Seriescited by 59
  5. 2022Transfer Learning with Deep Tabular Modelscited by 52
  6. 2023Soft Contrastive Learning for Time Seriescited by 49
  7. 2021HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Datacited by 38
  8. 2022Contrastive Learning for Unsupervised Domain Adaptation of Time Seriescited by 36
  9. 2023Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Seriescited by 16
  10. 2023Self-supervised Representation Learning From Random Data Projectorscited by 14
  11. 2023Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approachcited by 13
  12. 2024MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputationcited by 13
20 more
  1. 2022On the Importance of Clinical Notes in Multi-modal Learning for EHR Datacited by 10
  2. 2024CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domainscited by 8
  3. 2023Simple Contrastive Representation Learning for Time Series Forecastingcited by 7
  4. 2023On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Seriescited by 5
  5. 2024Using Self-supervised Learning Can Improve Model Fairnesscited by 5
  6. 2024Denoising-Aware Contrastive Learning for Noisy Time Seriescited by 5
  7. 2022Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomaxcited by 2
  8. 2024Multi-Modal Contrastive Learning for Online Clinical Time-Series Applicationscited by 2
  9. 2024EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognitioncited by 2
  10. 2025Decomposing and Revising What Language Models Generatecited by 2
  11. 2022Deep Normed Embeddings for Patient Representationcited by 1
  12. 2023M(otion)-mode Based Prediction of Ejection Fraction using Echocardiogramscited by 1
  13. 2024Evaluating Fairness in Self-supervised and Supervised Models for Sequential Datacited by 1
  14. 2024Unlocking Telemetry Potential: Self-Supervised Learning for Continuous Clinical Electrocardiogram Monitoringcited by 1
  15. 2023Language Model Training Paradigms for Clinical Feature Embeddingscited by 0
  16. 2024Revisiting Attention for Multivariate Time Series Forecastingcited by 0
  17. 2024On the Influence of Shape, Texture and Color for Learning Semantic Segmentationcited by 0
  18. 2024Contrast All the Time: Learning Time Series Representation from Temporal Consistencycited by 0
  19. 2025eMargin: Revisiting Contrastive Learning with Margin-Based Separationcited by 0
  20. 2025PeriodNet: Boosting the Potential of Attention Mechanism for Time Series Forecastingcited by 0