ArticleInternal and emergency medicine2025
Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.
Article in Internal and emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction.BMC medical informatics and decision making · 2026Pooled it
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Artificial Intelligence Models for Mortality and Outcome Prediction in Intensive Care Unit Sepsis: A Systematic Review.Journal of personalized medicine · 2026Review
- Development and Validation of a Machine Learning-Based Risk Assessment Tool for In-Hospital Mortality in Elderly Patients with Postoperative Hypoxemia Following Non-Cardiac Surgery.Journal of clinical medicine · 2026Article
- Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.Korean journal of anesthesiology · 2026Article
- Sepsis mortality prediction in ICU: align time zero and prevent leakage.Internal and emergency medicine · 2026Article
- Early prediction of renal replacement therapy within 24 hours after septic shock recognition in the emergency department using machine learning: a retrospective analysis of a prospectively collected multicenter registry.BMC emergency medicine · 2026Article
- Predictive Model of Dynamic Subphenotypes for 30-Day Mortality in Emergency Department Patients with Suspected Infection Using the Vital Signs of the First 24 Hours: An Analytical Cohort Study in a Tertiary Care Clinic.Journal of clinical medicine · 2026Article
- A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt Independence Criterion.Frontiers in big data · 2026Article
- Risk factors and prediction model for mortality risk in older adults septic patients.Frontiers in medicine · 2026Article
- Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.Frontiers in medicine · 2026Review
- Analysis and research on clinical factors and treatment of infection in oral and maxillofacial space infection.BMC oral health · 2025Article
- Machine learning model to predict mortality in patients with skin and soft tissue infection in emergency department.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Article
- Septic Shock in Hematological Malignancies: Role of Artificial Intelligence in Predicting Outcomes.Current oncology (Toronto, Ont.) · 2025Review
- A multicenter study on developing a prognostic model for severe fever with thrombocytopenia syndrome using machine learning.Frontiers in microbiology · 2025Article
- Interpretable Machine Learning Model for Early Mortality Prediction in Septic Patients Using Routine Post-Diagnosis Clinical Data: A Multicenter Study.Journal of inflammation research · 2025Article
- Personalized machine learning-based prognostic model for ICU-acquired bloodstream infections.Frontiers in cellular and infection microbiology · 2025Article
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5 authors.
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Abstract
Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsis outcomes early is vital. Despite machine learning's (ML) use in medical research, local validation within the Medical Information Mart for Intensive Care IV (MIMIC-IV) database is lacking. We aimed to devise a prognostic model, leveraging MIMIC-IV data, to predict sepsis mortality and validate it in a Chinese teaching hospital. MIMIC-IV provided patient data, split into training and internal validation sets. Four ML models logistic regression (LR), support vector machine (SVM), deep neural networks (DNN), and extreme gradient boosting (XGBoost) were employed. Shapley additive interpretation offered early and interpretable mortality predictions. Area under the ROC curve (AUROC) gaged predictive performance. Results were cross verified in a Chinese teaching hospital. The study included 27,134 sepsis patients from MIMIC-IV and 487 from China. After comparing, 52 clinical indicators were selected for ML model development. All models exhibited excellent discriminative ability. XGBoost surpassed others, with AUROC of 0.873 internally and 0.844 externally. XGBoost outperformed other ML models (LR: 0.829; SVM: 0.830; DNN: 0.837) and clinical scores (Simplified Acute Physiology Score II: 0.728; Sequential Organ Failure Assessment: 0.728; Oxford Acute Severity of Illness Score: 0.738; Glasgow Coma Scale: 0.691). XGBoost's hospital mortality prediction achieved AUROC 0.873, sensitivity 0.818, accuracy 0.777, specificity 0.768, and F1 score 0.551. We crafted an interpretable model for sepsis death risk prediction. ML algorithms surpassed traditional scores for sepsis mortality forecast. Validation in a Chinese teaching hospital echoed these findings.
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