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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2305.07877 |
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| _version_ | 1866916217903644672 |
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| author | Gunčar, Gregor Kukar, Matjaž Smole, Tim Moškon, Sašo Vovko, Tomaž Podnar, Simon Černelč, Peter Brvar, Miran Notar, Mateja Köster, Manca Jelenc, Marjeta Tušek Notar, Marko |
| author_facet | Gunčar, Gregor Kukar, Matjaž Smole, Tim Moškon, Sašo Vovko, Tomaž Podnar, Simon Černelč, Peter Brvar, Miran Notar, Mateja Köster, Manca Jelenc, Marjeta Tušek Notar, Marko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_07877 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Differentiating Viral and Bacterial Infections: A Machine Learning Model Based on Routine Blood Test Values Gunčar, Gregor Kukar, Matjaž Smole, Tim Moškon, Sašo Vovko, Tomaž Podnar, Simon Černelč, Peter Brvar, Miran Notar, Mateja Köster, Manca Jelenc, Marjeta Tušek Notar, Marko Machine Learning I.2.6; J.3 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. |
| title | Differentiating Viral and Bacterial Infections: A Machine Learning Model Based on Routine Blood Test Values |
| topic | Machine Learning I.2.6; J.3 |
| url | https://arxiv.org/abs/2305.07877 |