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 distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a Brier score of 0.129, and an area under the ROC curve (AUC) of 0.905, outperforming a CRP-based decision rule. Notably, the machine learning model enhanced accuracy within the CRP range of 10-40 mg/L, a range where CRP alone is less informative. These results highlight the advantage of integrating multiple blood parameters in diagnostics. The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management.

翻译：抗生素耐药性的日益威胁要求准确区分细菌与病毒感染，以合理使用抗生素。本研究开发了一种“病毒vs细菌”机器学习模型，利用16项常规血液检测结果、C反应蛋白浓度、生物性别和年龄来区分这两种感染类型。基于来自单一医疗中心的44,120例病例数据集，该模型实现了82.2%的准确率、79.7%的灵敏度、84.5%的特异性、0.129的Brier评分以及0.905的ROC曲线下面积，优于基于CRP的临床决策规则。值得注意的是，在CRP值处于10-40 mg/L这一区间（单独使用CRP信息量较低）时，该机器学习模型显著提升了准确率。这些结果凸显了整合多项血液参数在诊断中的优势。该“病毒vs细菌”模型为利用机器学习优化感染管理的高级诊断工具开辟了新途径。