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2023 | OriginalPaper | Buchkapitel

Machine Learning Algorithms to Predict Healthcare Associated Infections in a Neonatal Intensive Care Unit

verfasst von : Emma Montella, Marta Rosaria Marino, Arianna Scala, Teresa Angela Trunfio, Maria Triassi, Giovanni Improta

Erschienen in: Biomedical and Computational Biology

Verlag: Springer International Publishing

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Abstract

One of the most common causes of mortality and morbidity in neonatal intensive care units (NICU) are healthcare associated infections (HAIs). HAIs in newborns were recognized in 2016 as one of the six most common HAIs from the European Centre for Disease Prevention and Control (ECDC). Neonatal sepsis is a major contributor to neonatal morbidity and mortality. Predicting the onset of infections from a few characteristics of newborns can be crucial in combating this health problem. In this study, conducted at the NICU of the “Federico II” University Hospital in Naples between 2019 and 2020, four different Machine Learning (ML) algorithms was implemented and compared to assess their ability to predict the occurrence of infections from seven variables: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Gradient Boosted Tree (GBT). The four algorithms achieved very high percentages of accuracy, the best in particular was DT.

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Titel: Machine Learning Algorithms to Predict Healthcare Associated Infections in a Neonatal Intensive Care Unit
verfasst von: Emma Montella
Marta Rosaria Marino
Arianna Scala
Teresa Angela Trunfio
Maria Triassi
Giovanni Improta
Verlag: Springer International Publishing
Buch: Biomedical and Computational Biology
Print ISBN: 978-3-031-25190-0

Electronic ISBN: 978-3-031-25191-7

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-25191-7_38

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