mlsanityData to 14 Oct 2026

Benchmarking machine learning models on multi-centre eICU critical care dataset

arXiv 1910.00964 ↗First posted 2 Oct 2019cs.LG stat.MLcited by 35

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Abstract (arXiv)

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabled the possibility of establishing public benchmarks. Taking advantage of this opportunity, we propose a public benchmark suite to address four areas of critical care, namely mortality prediction, estimation of length of stay, patient phenotyping and risk of decompensation. We define each task and compare the performance of both clinical models as well as baseline and deep learning models using eICU critical care dataset of around 73,000 patients. This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model. We also investigate the impact of numerical variables as well as handling of categorical variables on each of the defined tasks. The source code, detailing our methods and experiments is publicly available such that anyone can replicate our results and build upon our work.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Oct 2019.

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Citing papers per year: 2021: 4; 2022: 4; 2023: 10; 2024: 4; 2025: 6; 2026: 7.

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  1. 2023EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Modelscited by 105
  2. 2022A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Predictioncited by 48
  3. 2021An Empirical Framework for Domain Generalization in Clinical Settingscited by 32
  4. 2023Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicinecited by 24
  5. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  6. 2025EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysiscited by 18
  7. 2021A comparison of approaches to improve worst-case predictive model performance over patient subpopulationscited by 12
  8. 2023Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Datacited by 8
  9. 2024Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clientscited by 7
  10. 2023One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Datacited by 6
  11. 2023Privacy-preserving patient clustering for personalized federated learningcited by 4
  12. 2024Towards Foundation Models for Critical Care Time Seriescited by 4
23 more
  1. 2022Looking for Out-of-Distribution Environments in Multi-center Critical Care Datacited by 3
  2. 2024CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analyticscited by 3
  3. 2023XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patientscited by 2
  4. 2025Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promisescited by 2
  5. 2026MILM: Large Language Models for Multimodal Irregular Time Series with Informative Samplingcited by 2
  6. 2022Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Databasecited by 1
  7. 2022Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Valuescited by 1
  8. 2023Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRscited by 1
  9. 2023Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Predictioncited by 1
  10. 2023CSAI: Conditional Self-Attention Imputation for Healthcare Time-seriescited by 1
  11. 2024Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activationscited by 1
  12. 2026A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarkingcited by 1
  13. 2026Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modelingcited by 1
  14. 2021Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learningcited by 0
  15. 2023Length of Stay prediction for Hospital Management using Domain Adaptationcited by 0
  16. 2025P-CAFE: Personalized Cost-Aware Incremental Feature Selection For Electronic Health Recordscited by 0
  17. 2025EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databasescited by 0
  18. 2025Transfer Learning with Distance Covariance for Random Forest: Error Bounds and an EHR Applicationcited by 0
  19. 2025Closing Gaps: An Imputation Analysis of ICU Vital Signscited by 0
  20. 2026DT-ICU: Towards Explainable Digital Twins for ICU Patient Monitoring via Multi-Modal and Multi-Task Iterative Inferencecited by 0
  21. 2026When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusioncited by 0
  22. 2026A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportabilitycited by 0
  23. 2026LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Modelscited by 0