ArticleHeliyon2024
Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- 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
- 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
- The Transcription Factor DDIT3 Regulates Macrophage Function by Inhibiting KLF10 to Attenuate ALI/ARDS Inflammation.Inflammation · 2026Article
- Mechanisms and therapeutics of immunometabolic reprogramming driving macrophage-ECs interactions in sepsis-associated ARDS from the gut-lung axis perspective.Frontiers in immunology · 2026Review
- Development and validation of a predictive model for postoperative acute respiratory distress syndrome in patients with type A aortic dissection based on the 2023 updated definition.Respiratory research · 2025Article
- Prediction of postoperative nausea and vomiting in patients undergoing sedated gastrointestinal endoscopy based on machine learning.Annals of medicine · 2025Article
- Review
- Predicting mortality and risk factors of sepsis related ARDS using machine learning models.Scientific reports · 2025Article
- Establishment and validation of predictive model of ARDS in critically ill patients.Journal of translational medicine · 2025Article
- Development and Validation of a Prediction Model for Respiratory Failure in Patients With Sepsis-Associated Acute Kidney Injury (SA-AKI) Within 48 Hours of Admission.Emergency medicine international · 2025Article
- Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.Scientific reports · 2024Article
- Article
- Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants.Journal of clinical medicine · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Acute respiratory distress syndrome (ARDS) is a fatal outcome of severe sepsis. Machine learning models are helpful for accurately predicting ARDS in patients with sepsis at an early stage. Objective: We aim to develop a machine-learning model for predicting ARDS in patients with sepsis in the intensive care unit (ICU). Methods: The initial clinical data of patients with sepsis admitted to the hospital (including population characteristics, clinical diagnosis, complications, and laboratory tests) were used to predict ARDS, and screen out the crucial variables. After comparing eight different algorithms, namely, XG boost, logistic regression, light GBM, random forest, GaussianNB, complement NB, support vector machine (SVM), and K nearest neighbors (KNN), rebuilding a prediction model with the best one. When remodeling with the best algorithm, 10% was randomly selected to test, and the remaining was trained for cross-validation. Using the area under the curve (AUC), sensitivity, accuracy, specificity, positive and negative predictive value, F1 score, kappa value, and clinical decision curve to evaluate the model's performance. Eventually, the application in the model illustrated by the SHAP package. Results: Ten critical features were screened utilizing the lasso method, namely, PaO Conclusion: Sepsis-associated ARDS can be accurately predicted early via a machine learning model based on existing clinical data. These findings are helpful for accurate identification and improvement of the prognosis in patients with sepsis-associated ARDS.
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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.