Observational studyThe Journal of international medical research2024
Comparison between XGboost model and logistic regression model for predicting sepsis after extremely severe burns.
Observational study in The Journal of international medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Development and external validation of a machine learning model for predicting the 28-day mortality risk in patients with sepsis complicated by acute respiratory failure in the ICU.Journal of intensive medicine · 2026Article
- Evaluating Open-Source Solutions for Computerized Inference of Infant Facial Affect.Developmental science · 2026Article
- Artificial intelligence-based prediction of postoperative sepsis in patients undergoing suction mini-percutaneous nephrolithotomy in real world practice: a multicenter analysis from the global Suction Lechnology Utility in Mini-PCNL Study (STUMPS) registry.World journal of urology · 2026Article
- Development of Machine Learning-Based Models to Predict Mortality in Intensive Care Unit Patients with Sepsis.Journal of inflammation research · 2026Article
- Efficacy and safety of Oliceridine versus Sufentanil in postoperative analgesia for burn skin grafting: a machine learning and SHAP-based cohort study.Frontiers in medicine · 2026Article
- Simultaneous prediction of early and delayed mortality in burn patients: a comparative machine learning analysis of feature importance in a single-center retrospective study.BMC medical informatics and decision making · 2025Article
- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- Machine-Learning Prediction of Bleeding After Endoscopic Submucosal Dissection for Early Gastric Cancer: A Multicenter Study.JGH open : an open access journal of gastroenterology and hepatology · 2025Article
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4 authors.
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Abstract
objectiveTo compare an Extreme Gradient Boosting (XGboost) model with a multivariable logistic regression (LR) model for their ability to predict sepsis after extremely severe burns.
methodsFor this observational study, patient demographic and clinical information were collected from medical records. The two models were evaluated using area under curve (AUC) of the receiver operating characteristic (ROC) curve.
resultsOf the 103 eligible patients with extremely severe burns, 20 (19%) were in the sepsis group, and 83 (81%) in the non-sepsis group. The LR model showed that age, admission time, body index (BI), fibrinogen, and neutrophil to lymphocyte ratio (NLR) were risk factors for sepsis. Comparing AUC of the ROC curves, the XGboost model had a higher predictive performance (0.91) than the LR model (0.88). The SHAP visualization tool indicated fibrinogen, NLR, BI, and age were important features of sepsis in patients with extremely severe burns.
conclusionsThe XGboost model was superior to the LR model in predictive efficacy. Results suggest that, fibrinogen, NLR, BI, and age were correlated with sepsis after extremely severe burns.
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