ArticleJournal of clinical medicine2021
Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study.European journal of pediatrics · 2026Article
- Personalized decision-making through AI solutions in pediatric emergency medicine: Focusing on febrile children.PLOS digital health · 2025Review
- Scoping review of clinical decision aids in the assessment and management of febrile infants under 90 days of age.BMC pediatrics · 2025Article
- Advancing risk factor identification for pediatric lobar pneumonia: the promise of machine learning technologies.Frontiers in pediatrics · 2025Article
- 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
- Use of Machine Learning to Differentiate Children With Kawasaki Disease From Other Febrile Children in a Pediatric Emergency Department.JAMA network open · 2023Article
- Predictor of Early Administration of Antibiotics and a Volume Resuscitation for Young Infants with Septic Shock.Antibiotics (Basel, Switzerland) · 2021Article
- Machine learning-based prediction of coronary care unit readmission: A multihospital validation study.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to develop and evaluate a machine learning (ML) model to predict invasive bacterial infections (IBIs) in young febrile infants visiting the emergency department (ED).
methodsThis retrospective study was conducted in the EDs of three medical centers across Taiwan from 2011 to 2018. We included patients age in 0-60 days who were visiting the ED with clinical symptoms of fever. We developed three different ML algorithms, including logistic regression (LR), supportive vector machine (SVM), and extreme gradient boosting (XGboost), comparing their performance at predicting IBIs to a previous validated score system (IBI score).
resultsDuring the study period, 4211 patients were included, where 126 (3.1%) had IBI. A total of eight, five, and seven features were used in the LR, SVM, and XGboost through the feature selection process, respectively. The ML models can achieve a better AUROC value when predicting IBIs in young infants compared with the IBI score (LR: 0.85 vs. SVM: 0.84 vs. XGBoost: 0.85 vs. IBI score: 0.70,
conclusionsAll ML models developed in this study outperformed the traditional scoring system in stratifying low-risk febrile infants after the standardized sensitivity level.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.