ArticleScientific reports2024
Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 4 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning application in the prediction of postoperative delirium among elderly patients: a systematic review and meta-analysis.Langenbeck's archives of surgery · 2026Pooled 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
- Prediction models for the occurrence and mortality of sepsis-associated lung injury: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Power, Duration, and Compliance: Reframing Risk of Ventilatory-Induced Lung Injury With the Risk-Adjusted Mechanical-Power Score.Critical care medicine · 2026Article
- Correlation analysis of the expression levels of serum endothelial cell adhesion molecule-1 and sirtuin 1 protein in acute respiratory distress syndrome.Journal of medical biochemistry · 2026Article
- Combining generative large language models and pretrained language models for predicting acute respiratory distress syndrome progression.Medical & biological engineering & computing · 2026Article
- Machine learning in ARDS: an intensivist's guide to artificial intelligence applications.Critical care (London, England) · 2026Review
- Review
- Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES.BMC gastroenterology · 2025Article
- Predicting mortality and risk factors of sepsis related ARDS using machine learning models.Scientific reports · 2025Article
- An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome.Frontiers in medicine · 2025Article
- Observational
- Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.Frontiers in cellular and infection microbiology · 2025Article
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5 authors.
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Abstract
objectiveThe objective was to establish a machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.
methods80% of sepsis patients selected randomly from the MIMIC-IV database, without prior pulmonary conditions and with nonpulmonary infection sites, were used to construct prediction models through machine learning techniques (including K-nearest neighbour, extreme gradient boosting, support vector machine, deep neural network, and decision tree methods). The remaining 20% of patients were utilized to validate the model's accuracy. Additionally, local data were employed for further model validation.
resultsA total of 11,409 patients were included, with the most common type of infection being bloodstream infection. A total of 7,632 (66.9%) patients developed nonpulmonary sepsis-associated ARDS (NPS-ARDS). Patients with NPS-ARDS had significantly longer ICU stays (6.2 ± 5.2 days vs. 4.4 ± 3.7 days, p < 0.01) and higher 28-day mortality rates (19.5% vs. 14.9%, p < 0.01). Both internal and external validation demonstrated that the model constructed with the extreme gradient boosting method had high accuracy. In the internal validation, the model predicted NPS-ARDS and mortality in such patients with accuracies of 77.5% and 71.8%, respectively. In the external validation, the model predicted NPS-ARDS and mortality in these patients with accuracies of 78.0% and 81.4%, respectively.
conclusionThe model established via the extreme gradient boosting method can predict the occurrence and mortality of nonpulmonary sepsis-associated ARDS to a certain extent.
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