Keywords
Infant Newborn
Neonatal Sepsis
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Abstract
Objective: To identify approaches and the effectiveness of Artificial Intelligence (AI) in predicting risk factors for neonatal sepsis. Methods: This is an integrative review conducted across the PubMed, Scopus, Web of Science, and IEEE databases using the following descriptors: "neonatal sepsis," "intensive care units, neonatal," "artificial intelligence," "machine learning," and "deep learning." Studies published between 2014 and 2024 that included neonatal sepsis in intensive care units were included. Results: The final sample consisted of 06 articles, with a higher concentration of publications in 2021. Most studies were conducted in the USA, and the predominant research designs were retrospective cohort studies. It was found that AI models, such as Random Forest, SVM, and Logistic Regression, are effective in predicting critical risk factors for neonatal sepsis, allowing for earlier and more precise clinical interventions, which can significantly improve clinical outcomes in vulnerable neonates. Final considerations: This review highlights the impact of AI models in predicting neonatal sepsis, demonstrating their potential for early clinical interventions. However, future research should focus on external validation and the careful integration of these models into clinical practice.
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