mlsanityData to 7 Oct 2026

Multitask learning and benchmarking with clinical time series data

arXiv 1703.07771 ↗First posted 22 Mar 2017stat.ML cs.LGcited by 287

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Abstract (arXiv)

Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification. We propose strong linear and neural baselines for all four tasks and evaluate the effect of deep supervision, multitask training and data-specific architectural modifications on the performance of neural models.

Key facts. Cited by 287 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 44; 2022: 44; 2023: 54; 2024: 49; 2025: 50; 2026: 46.

287 papers.

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  1. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
  2. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  3. 2023ForecastPFN: Synthetically-Trained Zero-Shot Forecastingcited by 102
  4. 2023EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Modelscited by 101
  5. 2021Time Series Prediction using Deep Learning Methods in Healthcarecited by 71
  6. 2024FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusioncited by 67
  7. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 66
  8. 2023Benchmarking Distribution Shift in Tabular Data with TableShiftcited by 64
  9. 2023GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphscited by 57
  10. 2024A Closer Look at AUROC and AUPRC under Class Imbalancecited by 56
  11. 2023Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Seriescited by 54
  12. 2022MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray imagescited by 53
38 more
  1. 2024DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistencycited by 51
  2. 2022A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Predictioncited by 48
  3. 2023Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Eventscited by 46
  4. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  5. 2022An Extensive Data Processing Pipeline for MIMIC-IVcited by 39
  6. 2021HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Datacited by 38
  7. 2022Contrastive Learning for Unsupervised Domain Adaptation of Time Seriescited by 36
  8. 2022GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learningcited by 35
  9. 2021Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologiescited by 34
  10. 2022Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modelingcited by 33
  11. 2021Neighborhood Contrastive Learning Applied to Online Patient Monitoringcited by 32
  12. 2021X-CAL: Explicit Calibration for Survival Analysiscited by 30
  13. 2021Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Seriescited by 30
  14. 2021Benchmarking emergency department triage prediction models with machine learning and large public electronic health recordscited by 30
  15. 2021Towards Generating Real-World Time Series Datacited by 26
  16. 2023Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicinecited by 24
  17. 2024Open (Clinical) LLMs are Sensitive to Instruction Phrasingscited by 23
  18. 2021Predicting Patient Outcomes with Graph Representation Learningcited by 21
  19. 2023Probabilistic Imputation for Time-series Classification with Missing Datacited by 21
  20. 2024RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Recordscited by 21
  21. 2025MIRA: Medical Time Series Foundation Model for Real-World Health Datacited by 21
  22. 2022Individual Privacy Accounting with Gaussian Differential Privacycited by 19
  23. 2022The Missing Indicator Method: From Low to High Dimensionscited by 19
  24. 2021Heterogeneous Multi-task Learning with Expert Diversitycited by 18
  25. 2023DuETT: Dual Event Time Transformer for Electronic Health Recordscited by 17
  26. 2023Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methodscited by 17
  27. 2023Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Seriescited by 16
  28. 2021MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Datasetcited by 15
  29. 2024Multi-modal Deep Learningcited by 15
  30. 2021AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Unitscited by 14
  31. 2022Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsiscited by 14
  32. 2023LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learningcited by 14
  33. 2023Self-supervised Representation Learning From Random Data Projectorscited by 14
  34. 2025Multimodal Medical Code Tokenizercited by 14
  35. 2022Multimodal data matters: language model pre-training over structured and unstructured electronic health recordscited by 13
  36. 2022Partitioned Variational Inference: A Framework for Probabilistic Federated Learningcited by 13
  37. 2021VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Modelscited by 12
  38. 2021A comparison of approaches to improve worst-case predictive model performance over patient subpopulationscited by 12

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