ArticlePloS one2020
Benchmarking machine learning models on multi-centre eICU critical care dataset.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Application of machine learning for the diagnosis and prognosis of sepsis-induced acute respiratory distress syndrome: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Factors underpinning the performance of implemented artificial intelligence-based patient deterioration prediction systems: reasons for selection and implications for hospitals and researchers.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- Cross-Silo Federated Learning for Predicting Successful Mechanical Ventilation Weaning Across 5 Intensive Care Unit Databases: Retrospective Database Analysis.JMIR medical informatics · 2026Article
- Mitigating algorithmic unfairness arising from forgetfulness of medical records in clinical artificial intelligence.Nature communications · 2026Article
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- Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data.Scientific reports · 2025Article
- Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity.IEEE transactions on knowledge and data engineering · 2025Article
- Low responsiveness of machine learning models to critical or deteriorating health conditions.Communications medicine · 2025Article
- Reliable generation of privacy-preserving synthetic electronic health record time series via diffusion models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery.PLOS digital health · 2024Article
- Fine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks.Proceedings. IEEE International Conference on Healthcare Informatics · 2024Article
- Safe and reliable transport of prediction models to new healthcare settings without the need to collect new labeled data.medRxiv : the preprint server for health sciences · 2023Article
- Patients' Views on AI for Risk Prediction in Shared Decision-Making for Knee Replacement Surgery: Qualitative Interview Study.Journal of medical Internet research · 2023Article
- Algorithmic fairness in artificial intelligence for medicine and healthcare.Nature biomedical engineering · 2023Review
- Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.International journal of environmental research and public health · 2023Article
- Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning.Nature machine intelligence · 2023Article
- Privacy-preserving patient clustering for personalized federated learning.Proceedings of machine learning research · 2023Article
- A comparison of approaches to improve worst-case predictive model performance over patient subpopulations.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.