Differentiating Viral and Bacterial Infections: A Machine Learning Model Based on Routine Blood Test Values
This addresses antibiotic resistance by improving infection diagnosis for medical practitioners, though it is incremental as it builds on existing diagnostic methods.
The study developed a machine learning model to differentiate viral from bacterial infections using routine blood test data, achieving 82.2% accuracy and an AUC of 0.905, outperforming a CRP-based rule.
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.