ArticleiScience2022
Evaluating machine learning models for sepsis prediction: A systematic review of methodologies.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 4 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
38 citing papers in PubMed, 4 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
- Prediction models for the occurrence and mortality of sepsis-associated lung injury: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- [S3 guideline on sepsis-prevention, diagnosis, therapy, and follow-up care-update 2025].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2025Guideline
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- A digital twin for tracking and forecasting glycemia with septic patients in ICUs.npj metabolic health and disease · 2026Article
- Prediction of high-flow nasal cannula failure in patients with sepsis by a preliminary risk stratification model based on the ROX index.BMC infectious diseases · 2026Article
- Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study.BMC medical informatics and decision making · 2026Article
- Evaluating deep learning sepsis prediction models in ICUs under distribution shift: a multi-centre retrospective cohort study.NPJ digital medicine · 2026Article
- Continuous Evaluation Frameworks for Retrospective Evaluation of Clinical Machine Learning Models.Critical care (London, England) · 2026Article
- Machine learning model for daily prediction of pediatric sepsis using Phoenix criteria.Pediatric research · 2026Article
- Predicting Pediatric Sepsis and Mortality Using Wearable Device Data and Machine Learning in Bangladesh.The American journal of tropical medicine and hygiene · 2026Observational
- Hyper-inflammation and immunosuppression: redefining sepsis therapy using modern approaches.Frontiers in pharmacology · 2026Review
- Machine learning glucose forecasting models for septic patients.Scientific reports · 2025Article
- Septic Shock in Hematological Malignancies: Role of Artificial Intelligence in Predicting Outcomes.Current oncology (Toronto, Ont.) · 2025Review
- A comparison of modeling approaches for static and dynamic prediction of central line-associated bloodstream infections using electronic health records (part 2): random forest models.Diagnostic and prognostic research · 2025Article
- Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database.European journal of medical research · 2025Article
- A methodological systematic review of validation and performance of sepsis real-time prediction models.NPJ digital medicine · 2025Article
- Artificial intelligence-driven prediction and interpretation of central line-associated bloodstream infections in ICU: insights from the MIMIC-IV database.Frontiers in public health · 2025Observational
- BOBM: an adaptive deep learning framework for extended-window sepsis prediction with cross-institutional generalizability.Frontiers in medicine · 2025Article
- When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Studies for sepsis prediction using machine learning are developing rapidly in medical science recently. In this review, we propose a set of new evaluation criteria and reporting standards to assess 21 qualified machine learning models for quality analysis based on PRISMA. Our assessment shows that (1.) the definition of sepsis is not consistent among the studies; (2.) data sources and data preprocessing methods, machine learning models, feature engineering, and inclusion types vary widely among the studies; (3.) the closer to the onset of sepsis, the higher the value of AUROC is; (4.) the improvement in AUROC is primarily due to using machine learning as a feature engineering tool; (5.) deep neural networks coupled with Sepsis-3 diagnostic criteria tend to yield better results on the time series data collected from patients with sepsis. The new evaluation criteria and reporting standards will facilitate the development of improved machine learning models for clinical applications.
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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.