ArticleInfectious diseases and therapy2022
Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study.
Article in Infectious diseases and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 4 of them syntheses that pooled it.
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Who cites it
91 citing papers in PubMed, 4 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
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Artificial Intelligence Models for Mortality and Outcome Prediction in Intensive Care Unit Sepsis: A Systematic Review.Journal of personalized medicine · 2026Review
- Development of a preoperative accuracy prediction model using machine learning for implant placement in static-guided surgery: retrospective observational study.International journal of implant dentistry · 2026Observational
- SeqBoost: a sequential explainable model for predicting ED revisits within 72 hours.BMC medical informatics and decision making · 2026Article
- Assessing plague risk across seven decades of environmental change in Inner Mongolia, China: An interpretable machine learning.iScience · 2026Article
- Predicting adolescent depression: an interpretable machine learning model.BMC medical informatics and decision making · 2026Article
- Predictive factors of Black American emerging adults' psychological flourishing: a random forest analysis.Scientific reports · 2026Article
- The relationship between urinary tract infections and sepsis: results from Mendelian randomization and MIMIC data study.World journal of urology · 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
- Prediction of death in burn patients infected with antibiotic-resistant Staphylococcus aureus using machine learning based techniques.BMC infectious diseases · 2026Article
- Artificial Intelligence Drives Advances in Multi-Omics Analysis and Precision Medicine for Sepsis.Biomedicines · 2026Review
- Optimizing sepsis mortality prediction using hybrid federated learning and explainable AI framework.Scientific reports · 2026Article
- Early prediction of in-hospital deterioration after emergency department admission using machine learning models.BMC emergency medicine · 2026Article
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- Optimization of glucocorticoid administration in patients with sepsis using reinforcement learning: a multicenter retrospective study.Frontiers in cellular and infection microbiology · 2026Article
- Development of an online prediction tool for immunotherapy-related adverse events in patients with advanced NSCLC based on machine learning and temporal validation.Frontiers in oncology · 2026Article
31 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
introductionThis study aimed to develop and validate an interpretable machine-learning model based on clinical features for early predicting in-hospital mortality in critically ill patients with sepsis.
methodsWe enrolled all patients with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV, v.1.0) database from 2008 to 2019. Lasso regression was used for feature selection. Seven machine-learning methods were applied to develop the models. The best model was selected based on its accuracy and area under curve (AUC) in the validation cohort. Furthermore, we employed the SHapley Additive exPlanations (SHAP) method to illustrate the effects of the features attributed to the model, and to analyze how the individual features affect the output of the model, and to visualize the Shapley value for a single individual.
resultsIn total, 8,817 patients with sepsis were eligible for participation, the median age was 66.8 years (IQR, 55.9-77.1 years), and 3361 of 8817 participants (38.1%) were women. After selection, 25 of a total 57 clinical parameters collected on day 1 after ICU admission remained associated with prognosis and were used for developing the machine-learning models. Among seven constructed models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance with an AUC of 0.884 and an accuracy of 89.5% in the validation cohort. Feature importance analysis showed that Glasgow Coma Scale (GCS) score, blood urea nitrogen, respiratory rate, urine output, and age were the top 5 features of the XGBoost model with the greatest impact. Furthermore, SHAP force analysis illustrated how the constructed model visualized the individualized prediction of death.
conclusionsWe have demonstrated the potential of machine-learning approaches for predicting outcome early in patients with sepsis. The SHAP method could improve the interpretability of machine-learning models and help clinicians better understand the reasoning behind the outcome.
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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.