ArticleScientific reports2020
Survival prediction of patients with sepsis from age, sex, and septic episode number alone.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 3 syntheses or guidelines pooled it, 66 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
- Pentraxin-3 as a predictive marker of mortality in sepsis: an updated systematic review and meta-analysis.Critical care (London, England) · 2022Pooled it
- Machine Learning Models for Sepsis: From Early Detection to Short- and Long-Term Prognosis.International journal of molecular sciences · 2026Article
- Temporal phenotyping and prognostic stratification of patients with sepsis through longitudinal clustering.BioData mining · 2025Article
- A Novel Knowledge Fusion Ensemble for Diagnostic Differentiation of Pediatric Pneumonia and Acute Bronchitis.Diagnostics (Basel, Switzerland) · 2025Article
- What Is Fair? Defining Fairness in Machine Learning for Health.Statistics in medicine · 2025Review
- Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap.NPJ digital medicine · 2025Review
- Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.Scientific reports · 2025Article
- Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions.Journal of intensive medicine · 2024Review
- In-hospital mortality, readmission, and prolonged length of stay risk prediction leveraging historical electronic patient records.JAMIA open · 2024Article
- Prediction of sepsis mortality in ICU patients using machine learning methods.BMC medical informatics and decision making · 2024Article
- Challenges and Advances in Biomarker Detection for Rapid and Accurate Sepsis Diagnosis: An Electrochemical Approach.Biosensors · 2024Review
- Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme.Journal of healthcare informatics research · 2024Article
- Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records.PLOS digital health · 2024Article
- A novel estimator for the two-way partial AUC.BMC medical informatics and decision making · 2024Article
- Ensemble machine learning reveals key features for diabetes duration from electronic health records.PeerJ. Computer science · 2024Article
- Ten quick tips for electrocardiogram (ECG) signal processing.PeerJ. Computer science · 2024Article
- The application of metagenomics, radiomics and machine learning for diagnosis of sepsis.Frontiers in medicine · 2024Article
- Fairness as an afterthought: An American perspective on fairness in model developer-clinician user collaborations.PLOS digital health · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a life-threatening condition caused by an exaggerated reaction of the body to an infection, that leads to organ failure or even death. Since sepsis can kill a patient even in just one hour, survival prediction is an urgent priority among the medical community: even if laboratory tests and hospital analyses can provide insightful information about the patient, in fact, they might not come in time to allow medical doctors to recognize an immediate death risk and treat it properly. In this context, machine learning can be useful to predict survival of patients within minutes, especially when applied to few medical features easily retrievable. In this study, we show that it is possible to achieve this goal by applying computational intelligence algorithms to three features of patients with sepsis, recorded at hospital admission: sex, age, and septic episode number. We applied several data mining methods to a cohort of 110,204 admissions of patients, and obtained high prediction scores both on this complete dataset (top precision-recall area under the curve PR AUC = 0.966) and on its subset related to the recent Sepsis-3 definition (top PR AUC = 0.860). Additionally, we tested our models on an external validation cohort of 137 patients, and achieved good results in this case too (top PR AUC = 0.863), confirming the generalizability of our approach. Our results can have a huge impact on clinical settings, allowing physicians to forecast the survival of patients by sex, age, and septic episode number alone.
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