ArticleJournal of translational medicine2020
Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.
Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 324 papers, 3 of them syntheses that pooled it.
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Who cites it
324 citing papers in PubMed, 3 syntheses or guidelines pooled it, 582 citations in OpenAlex.
- 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 mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis.BMC infectious diseases · 2024Pooled it
- Secondary fragility fractures after hip fracture surgery in four thousand, four hundred and eighteen older adults: risk factors and internal validation of an interpretable machine-learning model.International orthopaedics · 2026Article
- Graph representation and learning for drug administration prediction.BMC medical informatics and decision making · 2026Article
- 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
- Prognostic determinants of mortality and clinical outcomes in sepsis: a retrospective cohort study.BMC infectious diseases · 2026Article
- Explainable machine learning for estimation of elevated left ventricular filling pressure: a multicenter validation.Journal of echocardiography · 2026Article
- Machine learning-based risk prediction of 28-day mortality for sepsis patients with augmented renal clearance.Scientific reports · 2026Article
- Development of a mortality prediction nomogram for dementia patients using the MIMIC-IV database.Scientific reports · 2026Article
- Integrative Transcriptomic Analysis Identifies Shared Immune-Fibrotic Transcriptional Programs Across Crohn's Disease and Idiopathic Pulmonary Fibrosis.International journal of molecular sciences · 2026Article
- Identification of Mitochondrial Dysfunction-Related Candidate Biomarkers and Analysis of the Immune Cell Infiltration in Epilepsy.Molecular neurobiology · 2026Article
- Development of a Machine Learning Model for Distant Metastasis Risk Stratification in Acral Melanoma.Cancer reports (Hoboken, N.J.) · 2026Article
- Ensemble learning for predicting birth trauma using high-dimensional data in the neonatal intensive care unit.Scientific reports · 2026Article
- Machine learning-based models for intraoperative blood loss of retroperitoneal laparoscopic adrenalectomy.Surgical endoscopy · 2026Article
- Integrating serum ferritin and neutrophil-to-lymphocyte ratio with Sequential Organ Failure Assessment score improves mortality prediction in sepsis.World journal of methodology · 2026Article
- High prevalence and predictive modeling of compassion fatigue in geriatric nursing practice.BMC geriatrics · 2026Article
- Development of a Machine Learning-Based Predictive Model and Clinically Oriented Web Application for 30-Day Mortality Following Cardiac Surgery.Sensors (Basel, Switzerland) · 2026Article
- Transforming Intracerebral Hemorrhage Care with Artificial Intelligence: Opportunities, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Exploring potential biomarkers of NETosis-Related genes in spinal cord injury through machine learning and multi-omics analysis.Biochemistry and biophysics reports · 2026Article
264 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models.
methodsUsing the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model.
resultsA total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800-0.838], 0.797 [95% CI 0.781-0.813] and 0.857 [95% CI 0.839-0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value.
conclusionsUsing machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.
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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.