Differentiating Viral and Bacterial Infections: A Machine Learning Model
  Based on Routine Blood Test Values
Gregor Gunčar Matjaž Kukar Tim Smole Sašo Moškon Tomaž Vovko Simon Podnar Peter Černelč Miran Brvar Mateja Notar Manca Köster Marjeta Tušek Jelenc Marko Notar
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.