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),
Peter Černelč (Author),
Miran Brvar (Author),
Mateja Notar (Author),
Manca Köster (Author),
Marjeta Tušek Jelenc (Author),
Žiga Osterc (Author),
Marko Notar (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: |
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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 |