The growing threat of antibiotic resistance necessitates accurate
differentiation between bacterial and viral infections for proper antibiotic
administration. In this study, a Virus vs. Bacteria machine learning model was
developed to discern between these infection types using 16 routine blood test
results, C-reactive protein levels, biological sex, and age. With a dataset of
44,120 cases from a single medical center, the Virus vs. Bacteria model
demonstrated remarkable accuracy of 82.2%, a Brier score of 0.129, and an area
under the ROC curve of 0.91, surpassing the performance of traditional CRP
decision rule models. The model demonstrates substantially improved accuracy
within the CRP range of 10 40 mg/L, an interval in which CRP alone offers
limited diagnostic value for distinguishing between bacterial and viral
infections. These findings underscore the importance of considering multiple
blood 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 support
enhanced clinical decision-making in managing infections.

为应对抗生素耐药性的日益严峻，该研究发展了一种 “病毒对细菌” 机器学习模型，使用 16 种常规血液检测结果、C - 反应蛋白水平、生物性别和年龄来区分这些感染类型，证明了该模型在诊断决策方面表现出更高的准确性和诊断价值。