ArticleAnnals of medicine and surgery (2012)2020
Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study.
Article in Annals of medicine and surgery (2012), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Early prognostication for ICU patients with combined respiratory and circulatory failure: an interpretable machine learning approach.Scientific reports · 2026Article
- Prognostic Significance of Computed Tomography Severity Score for Machine Learning Prediction of Intensive Care Unit Admission in COVID-19 Patients.Thoracic research and practice · 2026Article
- Predicting COVID-19 Mortality Risk Among Cardiovascular Disease Patients Using Artificial Intelligence Algorithms: A Retrospective Study on Clinical Data.Health science reports · 2026Article
- Severity Scores in SARS-CoV-2 Infection-A Comprehensive Bibliometric Review and Visualization Analysis.Epidemiologia (Basel, Switzerland) · 2026Review
- Mission imputable: Effects of missing data processing on infectious disease detection and prognosis.PloS one · 2026Article
- Data mining approach to predicting of death in patients with COVID-19.Journal of education and health promotion · 2026Article
- Enhancing mortality prediction in intensive care units: improving APACHE II, SOFA, and SAPS II scoring systems using long short-term memory.Internal and emergency medicine · 2025Article
- Use of machine learning models to predict mechanical ventilation, ECMO, and mortality in COVID-19.Frontiers in artificial intelligence · 2025Article
- Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.BMC public health · 2024Article
- A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals.Scientific reports · 2024Article
- Validation of a risk prediction model for COVID-19: the PERIL prospective cohort study.Future virology · 2023Article
- Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways.Health care management science · 2023Article
- Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data.Scientific reports · 2023Article
- An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems.Respiratory research · 2023Article
- Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks.Journal of education and health promotion · 2023Article
- Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients.Annals of operations research · 2022Article
- [Long short-term memory and Logistic regression for mortality risk prediction of intensive care unit patients with stroke].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2022Article
- Comparison of Severity of Illness Scores and Artificial Intelligence Models That Are Predictive of Intensive Care Unit Mortality: Meta-analysis and Review of the Literature.JMIR medical informatics · 2022Review
- A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest radiographs and clinical variables.ERJ open research · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
rationalePrediction of patients at risk for mortality can help triage patients and assist in resource allocation.
objectivesDevelop and evaluate a machine learning-based algorithm which accurately predicts mortality in COVID-19, pneumonia, and mechanically ventilated patients.
methodsRetrospective study of 53,001 total ICU patients, including 9166 patients with pneumonia and 25,895 mechanically ventilated patients, performed on the MIMIC dataset. An additional retrospective analysis was performed on a community hospital dataset containing 114 patients positive for SARS-COV-2 by PCR test. The outcome of interest was in-hospital patient mortality.
resultsWhen trained and tested on the MIMIC dataset, the XGBoost predictor obtained area under the receiver operating characteristic (AUROC) values of 0.82, 0.81, 0.77, and 0.75 for mortality prediction on mechanically ventilated patients at 12-, 24-, 48-, and 72- hour windows, respectively, and AUROCs of 0.87, 0.78, 0.77, and 0.734 for mortality prediction on pneumonia patients at 12-, 24-, 48-, and 72- hour windows, respectively. The predictor outperformed the qSOFA, MEWS and CURB-65 risk scores at all prediction windows. When tested on the community hospital dataset, the predictor obtained AUROCs of 0.91, 0.90, 0.86, and 0.87 for mortality prediction on COVID-19 patients at 12-, 24-, 48-, and 72- hour windows, respectively, outperforming the qSOFA, MEWS and CURB-65 risk scores at all prediction windows.
conclusionsThis machine learning-based algorithm is a useful predictive tool for anticipating patient mortality at clinically useful timepoints, and is capable of accurate mortality prediction for mechanically ventilated patients as well as those diagnosed with pneumonia and COVID-19.
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