SynthesisBMC infectious diseases2023
Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.
Synthesis in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
42 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled 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
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Global burden of varicella in children aged 5-9 y, relationship with vaccination policy indicators, and projected trends to 2030.Human vaccines & immunotherapeutics · 2026Article
- Machine learning models predicting extubation success in mechanically ventilated patients: a systematic review and meta-analysis.Intensive care medicine experimental · 2026Review
- Real-time prediction of trauma-induced coagulopathy using an inverted transformer (trauma-former): a methodological feasibility and simulation study based on the ADEMP framework.BMC medical research methodology · 2026Article
- Early Sepsis Detection Using Heterogeneous Structured ICU Data with Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.JMIR medical informatics · 2026Article
- Evaluating Artificial Intelligence Models for ICU Length of Stay Prediction: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2026Review
- Development and external validation of a machine learning model for predicting the 28-day mortality risk in patients with sepsis complicated by acute respiratory failure in the ICU.Journal of intensive medicine · 2026Article
- Explainable machine learning with routine biomarkers identifies culture-defined bacteremic urosepsis.Scientific reports · 2026Article
- Development and multicenter validation of an explainable machine learning diagnostic criteria for pediatric abdominal sepsis.NPJ digital medicine · 2026Article
- Development of a machine learning-based sepsis prediction model for real-world clinical settings in South Korea: a single-center retrospective study.Journal of Korean biological nursing science · 2026Article
- Hospital-Wide Sepsis Detection: A Machine Learning Model Based on Prospectively Expert-Validated Cohort.Journal of clinical medicine · 2026Article
- Exploring Primary and Interaction Effects of Minor Physical Anomalies: Development and Validation of Prediction Models Using Explainable Machine Learning Algorithms for Early-Onset Schizophrenia.Schizophrenia bulletin · 2026Article
- Deep Learning for Early Prediction of Adverse Events in Intensive Care Units Using Electronic Health Records: A Methodological Review.Risk management and healthcare policy · 2026Review
- Prospective economic evaluation of a predictive artificial intelligence model for sepsis: Effects on hospital costs and return on investment.PLOS global public health · 2026Article
- Machine learning for prediction of in-ICU mortality in sepsis: an observational study using the MIMIC IV database.Archives of medical science : AMS · 2026Article
- A Correlation between Sequential Organ Failure Assessment Scores and Biomarkers of Inflammation and Infection in Patients with Sepsis.Current medicinal chemistry · 2026Article
- The Infectious Diseases Orchestrator: Embracing AI Literacy in the Agentic Era.Open forum infectious diseases · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis is a life-threatening condition caused by an abnormal response of the body to infection and imposes a significant health and economic burden worldwide due to its high mortality rate. Early recognition of sepsis is crucial for effective treatment. This study aimed to systematically evaluate the performance of various machine learning models in predicting the onset of sepsis.
methodsWe conducted a comprehensive search of the Cochrane Library, PubMed, Embase, and Web of Science databases, covering studies from database inception to November 14, 2022. We used the PROBAST tool to assess the risk of bias. We calculated the predictive performance for sepsis onset using the C-index and accuracy. We followed the PRISMA guidelines for this study.
resultsWe included 23 eligible studies with a total of 4,314,145 patients and 26 different machine learning models. The most frequently used models in the studies were random forest (n = 9), extreme gradient boost (n = 7), and logistic regression (n = 6) models. The random forest (test set n = 9, acc = 0.911) and extreme gradient boost (test set n = 7, acc = 0.957) models were the most accurate based on our analysis of the predictive performance. In terms of the C-index outcome, the random forest (n = 6, acc = 0.79) and extreme gradient boost (n = 7, acc = 0.83) models showed the highest performance.
conclusionMachine learning has proven to be an effective tool for predicting sepsis at an early stage. However, to obtain more accurate results, additional machine learning methods are needed. In our research, we discovered that the XGBoost and random forest models exhibited the best predictive performance and were most frequently utilized for predicting the onset of sepsis.
trial registrationCRD42022384015.
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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.