ArticleCritical care medicine2021
Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example.
Article in Critical care medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 130 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Machine Learning in Intensive Care to Reduce Adverse Events, Complications, and Life-threatening Events (MIRACLE): Evaluation of Clinical Implementation of Machine Learning Based Decision Support for ICU Discharge
Development of an ICU Physiological State Space Monitor Based on the ICCA Database: A Single-Center Retrospective Observational Study
Who cites it
130 citing papers in PubMed, 4 syntheses or guidelines pooled it, 246 citations in OpenAlex.
- Deep learning models for ICU readmission prediction: a systematic review and meta-analysis.Critical care (London, England) · 2025Pooled it
- Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.BMC medical informatics and decision making · 2025Pooled it
- Sex and gender considerations in randomized controlled trials in critical care nephrology: a meta-epidemiologic study.BMC medicine · 2025Pooled it
- External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Interpretable Machine Learning for In-Hospital Mortality Prediction in ICU Patients Using First-24-Hour Routine Vital Signs: A SHAP-Based MIMIC-IV Study.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluating the generalisability of LiSep LSTM for early prediction of septic shock across US and european cohorts.Scientific reports · 2026Article
- Generalization, Cross-ICU Transfer, and Explainability of a Mortality and Time-to-Discharge Framework for the Intensive Care Unit.Journal of clinical medicine · 2026Article
- Challenges and opportunities in ECMO monitoring: A mixed-methods appraisal.The journal of extra-corporeal technology · 2026Review
- The LoVe score: Development and validation of a bedside clinical index to identify patients at risk for prolonged invasive mechanical ventilation.Journal of the Intensive Care Society · 2026Article
- Cross-Silo Federated Learning for Predicting Successful Mechanical Ventilation Weaning Across 5 Intensive Care Unit Databases: Retrospective Database Analysis.JMIR medical informatics · 2026Article
- Real-time prediction of atrial fibrillation in intensive care unit: a meta-learning approach.JAMIA open · 2026Article
- Effect of Weight-based versus Absolute Norepinephrine Dosing on Mortality Risk in Obese Patients with Septic Shock: An Observational, Multicohort, Retrospective Study.Anesthesiology · 2026Observational
- Power, Duration, and Compliance: Reframing Risk of Ventilatory-Induced Lung Injury With the Risk-Adjusted Mechanical-Power Score.Critical care medicine · 2026Article
- Reproducible clinical archetypes in acute respiratory failure: a multi-cohort trajectory analysis.Intensive care medicine · 2026Article
- Early prognostication for ICU patients with combined respiratory and circulatory failure: an interpretable machine learning approach.Scientific reports · 2026Article
- HRRT: hierarchical reinforcement learning for renal replacement therapy decision support.NPJ digital medicine · 2026Article
- FIRST-ICU: forecasting interventions and risk stratification in the ICU using graph neural network autoencoders.NPJ digital medicine · 2026Article
- Prospective and external evaluation of an AI model for continuous and early prediction of moderate and severe AKI in critically ill patients.Intensive care medicine experimental · 2026Article
- Respiratory mechanics and patient-ventilator interaction dataset from the ICU.Scientific data · 2026Article
- MAP: evaluation and multi-agent enhancement of large language models for inpatient pathways.npj health systems · 2026Article
70 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors at 8 institutions in 4 countries.
Funding
Abstract
objectivesCritical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data.
settingUniversity hospital ICU. SUBJECTS: Data from ICU patients admitted between 2003 and 2016.
interventionsWe used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database. MEASUREMENTS AND MAIN
resultsAmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous.
conclusionsTechnical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.