ArticleDiagnostics (Basel, Switzerland)2020
Using Machine Learning to Predict Bacteremia in Febrile Children Presented to the Emergency Department.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 28 citations in OpenAlex.
- Machine learning-based prediction of fever among under-five children in Ethiopia: A national-level study.PloS one · 2026Article
- Factors associated with positive blood cultures in children in nine African and Asian countries: the ACORN2 surveillance network.BMJ global health · 2025Article
- Leveraging innovative diagnostics as a tool to contain superbugs.Antonie van Leeuwenhoek · 2025Review
- Early detection of bloodstream infection in critically ill children using artificial intelligence.Acute and critical care · 2024Article
- A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients.Diagnostics (Basel, Switzerland) · 2024Article
- A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.Scientific reports · 2024Article
- Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters.BMC infectious diseases · 2023Article
- Hemoglobin and Its Z Score Reference Intervals in Febrile Children: A Cohort Study of 98,572 Febrile Children.Children (Basel, Switzerland) · 2023Article
- Use of Machine Learning to Differentiate Children With Kawasaki Disease From Other Febrile Children in a Pediatric Emergency Department.JAMA network open · 2023Article
- Benchmarking emergency department prediction models with machine learning and public electronic health records.Scientific data · 2022Article
- Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study.Diagnostics (Basel, Switzerland) · 2022Article
- Bacteremia detection from complete blood count and differential leukocyte count with machine learning: complementary and competitive with C-reactive protein and procalcitonin tests.BMC infectious diseases · 2022Article
- Observational
- Predictor of Early Administration of Antibiotics and a Volume Resuscitation for Young Infants with Septic Shock.Antibiotics (Basel, Switzerland) · 2021Article
- An Artificial Intelligence Approach to Bloodstream Infections Prediction.Journal of clinical medicine · 2021Article
- Article
- Machine learning-based prediction of coronary care unit readmission: A multihospital validation study.Digital healthArticle
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Blood culture is frequently used to detect bacteremia in febrile children. However, a high rate of negative or false-positive blood culture results is common at the pediatric emergency department (PED). The aim of this study was to use machine learning to build a model that could predict bacteremia in febrile children. We conducted a retrospective case-control study of febrile children who presented to the PED from 2008 to 2015. We adopted machine learning methods and cost-sensitive learning to establish a predictive model of bacteremia. We enrolled 16,967 febrile children with blood culture tests during the eight-year study period. Only 146 febrile children had true bacteremia, and more than 99% of febrile children had a contaminant or negative blood culture result. The maximum area under the curve of logistic regression and support vector machines to predict bacteremia were 0.768 and 0.832, respectively. Using the predictive model, we can categorize febrile children by risk value into five classes. Class 5 had the highest probability of having bacteremia, while class 1 had no risk. Obtaining blood cultures in febrile children at the PED rarely identifies a causative pathogen. Prediction models can help physicians determine whether patients have bacteremia and may reduce unnecessary expenses.
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