ArticleScientific reports2024
Random forest differentiation of Escherichia coli in elderly sepsis using biomarkers and infectious sites.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Molecular subtyping and immune microenvironment heterogeneity in pediatric influenza-associated prolonged multiple organ dysfunction syndrome.Translational pediatrics · 2026Article
- Explainable machine learning for early detection ofFrontiers in cellular and infection microbiology · 2026Article
- A Retrospective Analysis of the Predictive Role of RDW, MPV, and MPV/PLT Values in 28-Day Mortality of Geriatric Sepsis Patients: Associations with APACHE II and SAPS II Scores.Medicina (Kaunas, Lithuania) · 2025Article
- Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections.Scientific reports · 2024Article
- Aloe-emodin plus TIENAM ameliorate cecal ligation and puncture-induced sepsis in mice by attenuating inflammation and modulating microbiota.Frontiers in microbiology · 2024Article
- Identifying key blood markers for bacteremia in elderly patients: insights into bacterial pathogens.Frontiers in cellular and infection microbiology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This study addresses the challenge of accurately diagnosing sepsis subtypes in elderly patients, particularly distinguishing between Escherichia coli (E. coli) and non-E. coli infections. Utilizing machine learning, we conducted a retrospective analysis of 119 elderly sepsis patients, employing a random forest model to evaluate clinical biomarkers and infection sites. The model demonstrated high diagnostic accuracy, with an overall accuracy of 87.5%, and impressive precision and recall rates of 93.3% and 87.5%, respectively. It identified infection sites, platelet distribution width, reduced platelet count, and procalcitonin levels as key predictors. The model achieved an F1 Score of 90.3% and an area under the receiver operating characteristic curve of 88.0%, effectively differentiating between sepsis subtypes. Similarly, logistic regression and least absolute shrinkage and selection operator analysis underscored the significance of infectious sites. This methodology shows promise for enhancing elderly sepsis diagnosis and contributing to the advancement of precision medicine in the field of infectious diseases.
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Registered trials
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