ArticleJournal of pediatric hematology/oncology2025
Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.
Article in Journal of pediatric hematology/oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Short-chain fatty acid-producing microbes differentiate non-infectious and infectious neutropenic fever in leukemia.mSystems · 2026Article
- Pediatric sequential organ failure assessment score predicts prognosis in children with acute lymphoblastic leukemia and sepsis: association with early multiple organ dysfunction.American journal of cancer research · 2026Article
- A multidimensional nomogram for clinical risk stratification of bloodstream infection and mortality in hematologic malignancies.Frontiers in cellular and infection microbiology · 2026Article
- Septic Shock in Hematological Malignancies: Role of Artificial Intelligence in Predicting Outcomes.Current oncology (Toronto, Ont.) · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in pediatric patients with cancer who presenting febrile neutropenia (FN) at emergency department (ED) visit. MATERIALS AND
methodsA retrospective study was conducted on patients, younger than 18 years of age, who visited a tertiary university-affiliated hospital ED due to FN between January 2004 and August 2022. ML models, based on XGBoost, were developed for BSI and SS prediction.
resultsAfter applying the exclusion criteria, we identified 4423 FN events during the study period. We identified 195 (4.4%) BSI and 107 (2.4%) SS events. The BSI and SS models demonstrated promising performance, with area under the receiver operating characteristic curve values of 0.87 and 0.88, respectively, which were superior to those of the logistic regression models. Clinical features, including body temperature, some laboratory results, vital signs, and diagnosis of acute myeloblastic leukemia were identified as significant predictors.
conclusionsThe ML-based prediction models, which use data obtainable at ED visits may be valuable tools for ED physicians to predict BSI or SS.
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