Development of a host-signature-based machine learning model to diagnose bacterial and viral infections in febrile children.

Background: Early aetiological diagnosis is critical for the management of febrile children with infectious illness, as it strongly influences the choice of appropriate medication and can affect a child's complications and outcome. New diagnostic strategies based on host genes have recently been developed and have achieved high accuracy and clinical practicability. In this study, through integrative bioinformatics analysis, we aimed to construct artificial neural network (ANN, multilayer perceptron) and random forest (RF) models based on host gene signatures to diagnose bacterial or viral (B/V) infection in febrile children. Results: Transcriptome data from the whole blood of children were collected from a public database. Of these, 384 febrile young children (definite bacterial: Conclusions: A five-gene host signature (IFIT2, SLPI, IFI27, LCN2, and PI3) was identified and successfully used to construct an RF model that distinguishes B/V infection in febrile children, achieving 85.3% accuracy, 95.1% sensitivity, and 80.0% specificity, and to construct an ANN model that achieves 92.4% accuracy, 86.8% sensitivity, and 95% specificity.