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The growing threat of antibiotic resistance necessitates accuratedifferentiation between bacterial and viral infections for proper antibioticadministration. In this study, a Virus vs. Bacteria machine learning model wasdeveloped to discern between these infection types using 16 routine blood testresults, C-reactive protein levels, biological sex, and age. With a dataset of44,120 cases from a single medical center, the Virus vs. Bacteria modeldemonstrated remarkable accuracy of 82.2%, a Brier score of 0.129, and an areaunder the ROC curve of 0.91, surpassing the performance of traditional CRPdecision rule models. The model demonstrates substantially improved accuracywithin the CRP range of 10 40 mg/L, an interval in which CRP alone offerslimited diagnostic value for distinguishing between bacterial and viralinfections. These findings underscore the importance of considering multipleblood parameters for diagnostic decision-making and suggest that the Virus vs.Bacteria model could contribute to the creation of innovative diagnostic tools.Such tools would harness machine learning and relevant biomarkers to supportenhanced clinical decision-making in managing infections.

Differentiating Viral and Bacterial Infections: A Machine Learning Model Based on Routine Blood Test Values