Prediction of bloodstream infection using machine learning based primarily on biochemical data

Abstract Early diagnosis of bloodstream infection (BSI) is crucial for informed antibiotic use. This study developed a machine learning approach for early BSI detection using a comprehensive dataset from Rigshospitalet, Denmark (2010–2020). The dataset included 144,398 samples from adult patients, c...

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Bibliographic Details
Main Authors:	Ramtin Zargari Marandi, Frederik Boetius Hertz, Jesper Qvist Thomassen, Steen Christian Rasmussen, Ruth Frikke-Schmidt, Niels Frimodt-Møller, Karen Leth Nielsen, Cameron Ross MacPherson
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-05-01
Series:	Scientific Reports
Subjects:	Interpretability Real-world data Clinical utility Artificial intelligence Diagnosis Classification models
Online Access:	https://doi.org/10.1038/s41598-025-01821-6
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Prediction of bloodstream infection using machine learning based primarily on biochemical data

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