Development of a machine learning-based prediction model for serious bacterial infections in febrile young infants.

Background: To develop and validate machine learning (ML)-based models to predict serious bacterial infections (SBIs) in febrile infants aged ≤90 days.

Methods: This retrospective study analysed data from febrile infants (≥38.0℃) aged ≤90 days.

Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.

Objectives: This 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 Methods: A 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.

Impact of initial vancomycin pharmacokinetic/pharmacodynamic parameters on the clinical and microbiological outcomes of methicillin-resistant Staphylococcus aureus bacteremia in children.

Optimal vancomycin exposure is important to minimize treatment failure of methicillin-resistant Staphylococcus aureus (MRSA) bacteremia. We aimed to analyze the impact of initial vancomycin pharmacokinetic/pharmacodynamic (PK/PD) parameters, including the initial vancomycin C trough and the area under the curve (AUC)/minimal inhibitory concentration (MIC) on the outcomes of pediatric MRSA bacteremia. The study population consisted of hospitalized children aged between 2 months and 18 years with MRSA bacteremia, in whom C trough was measured at least one time within the time period of January 2010 to March 2018.