ArticlePediatrics2020
Machine Learning To Predict Serious Bacterial Infections in Young Febrile Infants.
Article in Pediatrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study.European journal of pediatrics · 2026Article
- Development of a prediction model for infant hospitalisation and death using clinical features assessed by community health workers during routine postnatal home visits in Dhaka, Bangladesh.BMJ paediatrics open · 2026Article
- Transforming Pediatric Care Through AI: Bridging the Digital Divide in Health Informatics.Online journal of public health informatics · 2026Article
- Reimagining Pediatrics in a World of Artificial Intelligence: Will We Be Empowered or Imperiled?Mayo Clinic proceedings. Digital health · 2025Review
- Can general purpose large language models assist pediatricians in predicting infants with serious bacterial infection?BMC medical informatics and decision making · 2025Article
- Development of a machine learning-based prediction model for serious bacterial infections in febrile young infants.BMJ paediatrics open · 2025Article
- Learning-Based Models for Predicting IVIG Resistance and Coronary Artery Lesions in Kawasaki Disease: A Review of Technical Aspects and Study Features.Paediatric drugs · 2025Review
- Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia.BMJ paediatrics open · 2025Observational
- Scoping review of clinical decision aids in the assessment and management of febrile infants under 90 days of age.BMC pediatrics · 2025Article
- Prediction of Postoperative Pain and Side Effects of Patient-Controlled Analgesia in Pediatric Orthopedic Patients Using Machine Learning: A Retrospective Study.Journal of clinical medicine · 2025Article
- Machine learning model for predicting urinary tract infection risk in febrile children under 3 years of age.Frontiers in pediatrics · 2025Article
- Performance of clinical decision aids (CDA) for the care of young febrile infants: a multicentre prospective cohort study conducted in the UK and Ireland.EClinicalMedicine · 2024Article
- Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset.Scientific reports · 2024Article
- Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.Paediatric drugs · 2024Review
- Predicting community acquired bloodstream infection in infants using full blood count parameters and C-reactive protein; a machine learning study.European journal of pediatrics · 2024Article
- Differentiating viral and bacterial infections: A machine learning model based on routine blood test values.Heliyon · 2024Article
- A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit.PloS one · 2024Article
- Clinical Timing-Sequence Warning Models for Serious Bacterial Infections in Adults Based on Machine Learning: Retrospective Study.Journal of medical Internet research · 2023Article
- Febrile infants risk score at triage (FIRST) for the early identification of serious bacterial infections.Scientific reports · 2023Observational
- Machine learning applications on neonatal sepsis treatment: a scoping review.BMC infectious diseases · 2023Article
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Abstract
backgroundRecent decision rules for the management of febrile infants support the identification of infants at higher risk of serious bacterial infections (SBIs) without the performance of routine lumbar puncture. We derive and validate a model to identify febrile infants ≤60 days of age at low risk for SBIs using supervised machine learning approaches.
methodsWe conducted a secondary analysis of a multicenter prospective study performed between December 2008 and May 2013 of febrile infants. Our outcome was SBI, (culture-positive urinary tract infection, bacteremia, and/or bacterial meningitis). We developed and validated 4 supervised learning models: logistic regression, random forest, support vector machine, and a single-hidden layer neural network.
resultsA total of 1470 patients were included (1014 >28 days old). One hundred thirty-eight (9.3%) had SBIs (122 urinary tract infections, 20 bacteremia, and 8 meningitis; 11 with concurrent SBIs). Using 4 features (urinalysis, white blood cell count, absolute neutrophil count, and procalcitonin), we demonstrated with the random forest model the highest specificity (74.9, 95% confidence interval: 71.5%-78.2%) with a sensitivity of 98.6% (95% confidence interval: 92.2%-100.0%) in the validation cohort. One patient with bacteremia was misclassified. Among 1240 patients who received a lumbar puncture, this model could have prevented 849 (68.5%) such procedures.
conclusionsWe derived and internally validated a supervised learning model for the risk-stratification of febrile infants. Although computationally complex, lacking parameter cutoffs, and in need of external validation, this strategy may allow for reductions in unnecessary procedures, hospitalizations, and antibiotics while maintaining excellent sensitivity.
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