ArticleBMC medical informatics and decision making2024
Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients.Renal failure · 2026Article
- Artificial Intelligence Models for Mortality and Outcome Prediction in Intensive Care Unit Sepsis: A Systematic Review.Journal of personalized medicine · 2026Review
- Development and external validation of the HCH and HPMS prognostic indices for sepsis: a retrospective model development study using a Multi-Objective Non-Newtonian Fluid optimization algorithm.BMC medical informatics and decision making · 2026Article
- Machine learning-based analysis of prognostic factors in patients with tuberculous meningitis.BMC infectious diseases · 2026Article
- Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis.Journal of personalized medicine · 2026Review
- Development of a stacking model for personalized treatment of Crohn's disease: leveraging routine clinical features to forecast infliximab response.Frontiers in pharmacology · 2026Article
- Predicting post-ESD bleeding in elderly patients with early gastrointestinal cancer: an explainable model based on endoscopic features.Frontiers in medicine · 2026Article
- Machine learning for predicting clinical outcomes of hospitalised children: a systematic review of applications in low- and middle-income countries.EClinicalMedicine · 2026Review
- Development and Validation of a 28-Day Mortality Prediction Model for Patients with Sepsis Complicated by Autoimmune Diseases Using Two Machine Learning Methods.Journal of inflammation research · 2026Article
- Sepsis biomarkers: recent advances and future perspectives.Frontiers in immunology · 2026Review
- Improving Sepsis Mortality Prediction With Machine Learning Using Full Region Synthetic Sampling Approach.Health science reports · 2025Article
- The Potential of Artificial Intelligence in the Diagnosis and Prognosis of Sepsis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis.BMC infectious diseases · 2025Article
- Machine Learning-Based Prediction Model for Predicting the Effect of the Serum γKlotho Level on Susceptibility to Coronary Heart Disease.Vascular health and risk management · 2025Article
- Personalized machine learning-based prognostic model for ICU-acquired bloodstream infections.Frontiers in cellular and infection microbiology · 2025Article
- Early Mortality Prediction in Intensive Care Unit Patients Based on Serum Metabolomic Fingerprint.International journal of molecular sciences · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database.
methodsA cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model's prediction.
resultsUsing the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score.
conclusionsIn conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.
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