ArticleHeliyon2024
Differentiating viral and bacterial infections: A machine learning model based on routine blood test values.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- A 13-year cohort study using clinical machine learning to differentiate bacterial and viral infections in young infants in a dengue hyperendemic region.Journal of tropical pediatrics · 2026Article
- Decoupling of Respiratory Virus Positivity and Host Inflammatory Response: A 16-Year Longitudinal Study.Microorganisms · 2026Article
- Machine learning-based prediction of fever among under-five children in Ethiopia: A national-level study.PloS one · 2026Article
- Early risk stratification for carbapenem resistance amongFrontiers in cellular and infection microbiology · 2026Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- The value of common blood parameters for the differential diagnosis of respiratory tract infections in children.AMB Express · 2025Article
- Immune Signatures Distinguish Pure and Mixed Sepsis in Critical COVID-19: A Retrospective Cohort Study.Journal of inflammation research · 2025Article
- Respiratory infections in the post-COVID-19 era: impact, prevalence, and clinical characteristics of bacterial and viral co-infections.Frontiers in medicine · 2025Article
- Antibiotics Usage and Avoidance in Germany and Poland: Attitudes and Knowledge of Patients, Physicians, and Pharmacists.Antibiotics (Basel, Switzerland) · 2024Article
- Data-driven rapid detection ofHeliyon · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.