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

• URL: http://arxiv.org/abs/2305.07877v3
• Date: Mon, 22 Apr 2024 19:28:22 GMT
• Title: Differentiating Viral and Bacterial Infections: A Machine Learning Model Based on Routine Blood Test Values
• Authors: 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,
• Abstract summary: The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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