Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections
Abstract Artificial intelligence (AI) models are promising tools for predicting antimicrobial susceptibility in gram-negative bloodstream infections (GN-BSI). Single-center study on hospitalized patients with GN-BSI, over 7-year period, aimed to predict resistance to fluoroquinolones (FQ-R), third g...
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| Main Authors: | Cecilia Bonazzetti, Ettore Rocchi, Alice Toschi, Nicolas Riccardo Derus, Claudia Sala, Renato Pascale, Matteo Rinaldi, Caterina Campoli, Zeno Adrien Igor Pasquini, Beatrice Tazza, Armando Amicucci, Milo Gatti, Simone Ambretti, Pierluigi Viale, Gastone Castellani, Maddalena Giannella |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01696-x |
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