Differentiating viral and bacterial infections

ǂa ǂmachine learning model based on routine blood test values

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

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.

Keywords

virusi;bakterije;strojno učenje;viruses;bacteria;machine learning;

Data

Language:	English
Year of publishing:	2024
Typology:	1.01 - Original Scientific Article
Organization:	UL FKKT - Faculty of Chemistry and Chemical Technology
UDC:	578:579:004.85
COBISS:	194741507
ISSN:	2405-8440
Views:	25
Downloads:	0
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	virusi;bakterije;strojno učenje;
Type (COBISS):	Article
Pages:	str. 1-16
Volume:	ǂVol. ǂ10
Issue:	ǂiss. ǂ8, [article no.] e29372
Chronology:	30 Apr. 2024
DOI:	10.1016/j.heliyon.2024.e29372
ID:	23668377