Differentiating viral and bacterial infections : a machine learning model based on routine blood test values

Gunčar, Gregor (author), Kukar, Matjaž (author), Smole, Tim (author), Moškon, Sašo (author), Vovko, Tomaž (author), Podnar, Simon (author), Černelč, Peter (author), Brvar, Miran (author), Notar, Mateja (author), Köster, Manca (author), Tušek Jelenc, Marjeta (author), Osterc, Žiga (author), Notar, Marko (author)

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.

• Language: English
• Keywords: viruses, bacteria, machine learning
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FKKT - Faculty of Chemistry and Chemical Technology; FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: 16 str.
• Numbering: Vol. 10, iss. 8, art. e29372
• PID: 20.500.12556/RUL-156195
• UDC: 578:579:004.85
• ISSN on article: 2405-8440
• DOI: 10.1016/j.heliyon.2024.e29372
• COBISS.SI-ID: 194741507
• Publication date in RUL: 13.05.2024

Record is a part of a journal

• Title: Heliyon
• Publisher: Elsevier
• ISSN: 2405-8440
• COBISS.SI-ID: 21607432

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: virusi, bakterije, strojno učenje