ArticleDiagnostics (Basel, Switzerland)2022
Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Service-Specific Heterogeneity in Sepsis Variable Significance and Machine Learning Model Performance: A Stratified Analysis of the BIAlert Cohort.Journal of clinical medicine · 2026Article
- Intuitive Risk Equation for Post-Transplant Bloodstream Infection Prediction: A Symbolic Regression Approach.Biomedicines · 2026Article
- Developing Machine-Learning Models to Predict Bacteremia in Febrile Adults Presenting to the Emergency Department: A Retrospective Cohort Study from a Large Center.The western journal of emergency medicine · 2025Article
- Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.PLOS digital health · 2024Article
- Early detection of bloodstream infection in critically ill children using artificial intelligence.Acute and critical care · 2024Article
- Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study.Diagnostics (Basel, Switzerland) · 2022Article
- [New predictive models of bacteremia in the emergency department: a step forward].Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2022Review
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Authors and funding
8 authors at 1 institution in 1 country.
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Abstract
Early detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers' electronic medical records during a 12-year-period were extracted. Multi-layer perceptron (MLP), random forest, and gradient boosting algorithms were applied for machine learning analysis. Clinical data within 12 and 24 hours of blood culture were analyzed and compared. Out of 622,771 blood cultures, 38,752 episodes of bacteremia were identified. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUROC) of the prediction performance in 12- and 24-h data models was 0.762 (95% confidence interval (CI); 0.7617-0.7623) and 0.753 (95% CI; 0.7520-0.7529), respectively. AUROC of causative-pathogen subgroup analysis predictive value for
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