Observational studyMedicine2021
Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.
Observational study in Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Sepsis mortality prediction using machine learning and deep learning - a systematic review.BMC medical informatics and decision making · 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
- Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.Pharmacology research & perspectives · 2026Article
- Early Risk Stratification for 30-Day Mortality After In-Hospital Cardiac Arrest: SHAP Interpretable CatBoost Model with m-NUTRIC and Micronutrient Biomarkers.Journal of clinical medicine · 2026Article
- Optimizing sepsis mortality prediction using hybrid federated learning and explainable AI framework.Scientific reports · 2026Article
- An interpretable machine learning model for preoperative prediction of renal mass malignancy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.Frontiers in medicine · 2026Review
- Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records.Journal of translational medicine · 2025Article
- Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Sepsis Management: An Overview for Clinicians.Journal of clinical medicine · 2025Review
- Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study.BMC emergency medicine · 2025Observational
- Predicting mortality in intensive care unit patients with CAUTI using an interpretable machine learning model: a retrospective cohort study from MIMIC-IV database.Frontiers in medicine · 2025Article
- Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests-A Systematic Review.Biomedicines · 2024Review
- Machine Learning Differentiation of Autism Spectrum Sub-Classifications.Journal of autism and developmental disorders · 2024Article
- Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.Annals of surgical oncology · 2024Observational
- Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions.Journal of intensive medicine · 2024Review
- ChatGPT Predicts In-Hospital All-Cause Mortality for Sepsis: In-Context Learning with the Korean Sepsis Alliance Database.Healthcare informatics research · 2024Article
- Development and validation of an interpretable model for predicting sepsis mortality across care settings.Scientific reports · 2024Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
abstractSepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning models. However, there is a gap between the creation of different machine learning algorithms and their implementation in clinical practice.This study utilized data from the Medical Information Mart for Intensive Care III. We established and compared the gradient boosting decision tree (GBDT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM).A total of 3937 sepsis patients were included, with 34.3% mortality in the Medical Information Mart for Intensive Care III group. In our comparison of 5 machine learning models (GBDT, LR, KNN, RF, and SVM), the GBDT model showed the best performance with the highest area under the receiver operating characteristic curve (0.992), recall (94.8%), accuracy (95.4%), and F1 score (0.933). The RF, SVM, and KNN models showed better performance (area under the receiver operating characteristic curve: 0.980, 0.898, and 0.877, respectively) than the LR (0.876).The GBDT model showed better performance than other machine learning models (LR, KNN, RF, and SVM) in predicting the mortality of patients with sepsis in the intensive care unit. This could be used to develop a clinical decision support system in the future.
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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.