ArticlePatterns (New York, N.Y.)2024
A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Development and Interpretability Analysis of a Stacking Ensemble Model for Early Prediction of Nutritional Risk in Intensive Care Unit Patients: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Spatio-temporal epidemic forecasting with graph-based transformer.International journal of health geographics · 2026Article
- ClinicRealm: Re-evaluating large language models with conventional machine learning for non-generative clinical prediction tasks.NPJ digital medicine · 2026Article
- Evaluation and improvement of algorithmic fairness for COVID-19 severity classification using Explainable Artificial Intelligence-based bias mitigation.JAMIA open · 2026Article
- Personalizing Maintenance Rituximab in Follicular Lymphoma: A Machine Learning Framework for Risk-Benefit Optimization.Health data science · 2026Article
- PathCare: Integrating Clinical Pathway Information to Enable Healthcare Prediction at the Neuron Level.Bioengineering (Basel, Switzerland) · 2025Article
- Privacy-Preserving Federated Learning Framework for Multi-Source Electronic Health Records Prognosis Prediction.Sensors (Basel, Switzerland) · 2025Article
- Predictive factors for COVID-19 severity and mortality in hospitalized children.BMC infectious diseases · 2024Article
- 2023 Beijing Health Data Science Summit.Health data science · 2024Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks-outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care-which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling.
Indexed as
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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.