Observational studyCritical care medicine2024
The Impact of Multi-Institution Datasets on the Generalizability of Machine Learning Prediction Models in the ICU.
Observational study in Critical care medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 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.
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Who cites it
45 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Deep learning models for ICU readmission prediction: a systematic review and meta-analysis.Critical care (London, England) · 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
- A bibliometric analysis of artificial intelligence research in critical illness: a quantitative approach and visualization study.Frontiers in medicine · 2025Pooled it
- Compassionate care at end of life and nurses' coping with the patient's death: a cross-sectional correlational study.International journal of nursing studies advances · 2026Article
- Automated Assessment of OCT Angiography Image Quality Using the Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights Data Set.Ophthalmology science · 2026Article
- Research progress of AI assisted nano-SERS technology in rapid identification of multi species oral pathogenic biofilms.Mikrochimica acta · 2026Review
- Machine Learning Methods to Predict the Length of Stay for Acute Stroke: A Scoping Review and Meta-Analysis.Health care science · 2026Review
- Swarm Learning for the Detection of Middle Cerebral Artery Occlusion On CT Angiography.Clinical neuroradiology · 2026Article
- Toward universal representations of the living: Physiological invariance for transportable medical AI.PLOS digital health · 2026Review
- DCBM-Tri: a dual-channel bilinear mapping triplet model for early recognition of acute kidney injury in imbalanced cohorts.Scientific reports · 2026Article
- An interpretable whole-stream video based deep learning framework for surgical skill assessment of basic procedural elements.International journal of surgery (London, England) · 2026Article
- External Validation, Recalibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Multicenter Data.Critical care explorations · 2026Article
- Multi-source data integration through pooling and transfer learning improves generalizability and specialization of deep learning models for ICU mortality and length of stay prediction: a four-database external validation study.Critical care (London, England) · 2026Article
- Therapeutic Innovations in Nasopharyngeal Carcinoma: Current Strategies and Emerging Perspectives.Life (Basel, Switzerland) · 2026Review
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- The Impact of Evaluation Strategy on Sepsis Prediction Model Performance Metrics in Intensive Care Data: Retrospective Cohort Study.Journal of medical Internet research · 2026Observational
- Predicting the likelihood of hypoadrenocorticism in dogs using signalment and routine laboratory results with an ensemble machine learning predictive model.Journal of veterinary internal medicine · 2026Article
- Deep Learning for Early Prediction of Adverse Events in Intensive Care Units Using Electronic Health Records: A Methodological Review.Risk management and healthcare policy · 2026Review
- Translational Potential and Explainability of Artificial Intelligence-Based Clinical Decision Support for Adults in Intensive Care: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate the transferability of deep learning (DL) models for the early detection of adverse events to previously unseen hospitals.
designRetrospective observational cohort study utilizing harmonized intensive care data from four public datasets.
settingICUs across Europe and the United States. PATIENTS: Adult patients admitted to the ICU for at least 6 hours who had good data quality.
interventionsNone. MEASUREMENTS AND MAIN
resultsUsing carefully harmonized data from a total of 334,812 ICU stays, we systematically assessed the transferability of DL models for three common adverse events: death, acute kidney injury (AKI), and sepsis. We tested whether using more than one data source and/or algorithmically optimizing for generalizability during training improves model performance at new hospitals. We found that models achieved high area under the receiver operating characteristic (AUROC) for mortality (0.838-0.869), AKI (0.823-0.866), and sepsis (0.749-0.824) at the training hospital. As expected, AUROC dropped when models were applied at other hospitals, sometimes by as much as -0.200. Using more than one dataset for training mitigated the performance drop, with multicenter models performing roughly on par with the best single-center model. Dedicated methods promoting generalizability did not noticeably improve performance in our experiments.
conclusionsOur results emphasize the importance of diverse training data for DL-based risk prediction. They suggest that as data from more hospitals become available for training, models may become increasingly generalizable. Even so, good performance at a new hospital still depended on the inclusion of compatible hospitals during training.
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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.