ArticleScientific reports2025
Constructing an early warning model for elderly sepsis patients based on machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Analysis of the predictive value of 3-day cumulative energy deficit for 28-day mortality in patients with sepsis and nutritional risk: a retrospective study.Frontiers in nutrition · 2026Article
- Analysis and implications of validation results for a clinical data-based early warning model for geriatric sepsis.Frontiers in public health · 2026Article
- Closed-Loop digital therapeutics empowered by deep reinforcement learning and wearable sensing for precision orthopedic rehabilitation: a simulation-based proof-of-concept study.Frontiers in rehabilitation sciences · 2026Review
- Development and validation of machine learning models based on blood routine tests and tumor markers in early screening of primary bronchogenic lung cancer.Translational lung cancer research · 2025Article
- Artificial Intelligence in the Management of Infectious Diseases in Older Adults: Diagnostic, Prognostic, and Therapeutic Applications.Biomedicines · 2025Review
- Machine Learning Reveals the Value of Unconventional T Lymphocytes in Sepsis and Prognosis of Elderly Patients With Severe Lower Respiratory Tract Infections.Journal of clinical laboratory analysis · 2025Article
- A Predictive Model Based on Machine Learning Algorithm for Vein Thrombosis After Ovarian Cancer Resection.International journal of women's health · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Sepsis is a serious threat to human life. Early prediction of high-risk populations for sepsis is necessary especially in elderly patients. Artificial intelligence shows benefits in early warning. The aim of the study was to construct an early machine warning model for elderly sepsis patients and evaluate its performance. We collected elderly patients from General Hospital of Ningxia Medical University emergency department and intensive care unit from 01 January 2021 to 01 August 2023. The clinical data was divided into a training set and a test set. A total of 2976 patients and 12 features were screened. We used 8 machine learning models to build the warning model. In conclusion, we developed a model based on XGBoost with an AUROC of 0.971, AUPRC of 0.862, accuracy of 0.95, specificity of 0.964 and F1 score of 0.776. Of all the features, baseline APTT played the most important role, followed by baseline lymphocyte count. Higher level of baseline APTT and lower level of baseline lymphocyte count may indicate higher risk of sepsis occurrence. We developed a high-performance early warning model for sepsis in old age based on machine learning in order to facilitate early treatment but also need further external validation.
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