Differentiating viral and bacterial infections

ǂa ǂmachine learning model based on routine blood test values

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

Povzetek

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.

Ključne besede

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

Podatki

Jezik:	Angleški jezik
Leto izida:	2024
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FKKT - Fakulteta za kemijo in kemijsko tehnologijo
UDK:	578:579:004.85
COBISS:	194741507
ISSN:	2405-8440
Št. ogledov:	25
Št. prenosov:	0
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	virusi;bakterije;strojno učenje;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 1-16
Letnik:	ǂVol. ǂ10
Zvezek:	ǂiss. ǂ8, [article no.] e29372
Čas izdaje:	30 Apr. 2024
DOI:	10.1016/j.heliyon.2024.e29372
ID:	23668377