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Differentiating Viral and Bacterial Infections: A Machine Learning Model Based on Routine Blood Test Values
April 22, 2024, 4:42 a.m. | Gregor Gun\v{c}ar, Matja\v{z} Kukar, Tim Smole, Sa\v{s}o Mo\v{s}kon, Toma\v{z} Vovko, Simon Podnar, Peter \v{C}ernel\v{c}, Miran Brvar, Mateja Notar,
cs.LG updates on arXiv.org arxiv.org
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 …
abstract administration antibiotic resistance arxiv bacteria cs.lg differentiation infection machine machine learning machine learning model study test threat type types values viral virus
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