Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study

Emma Montella, Antonino Ferraro, Giancarlo Sperlì, Maria Triassi, Stefania Santini and Giovanni Improta
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Emma Montella: Department of Public Health, University of Naples “Federico”, 80125 Naples, Italy
Antonino Ferraro: Department of Information Technology and Electrical Engineering, University of Naples “Federico”, Via Claudio 21, 80125 Naples, Italy
Giancarlo Sperlì: Department of Information Technology and Electrical Engineering, University of Naples “Federico”, Via Claudio 21, 80125 Naples, Italy
Maria Triassi: Department of Public Health, University of Naples “Federico”, 80125 Naples, Italy
Stefania Santini: Department of Information Technology and Electrical Engineering, University of Naples “Federico”, Via Claudio 21, 80125 Naples, Italy
Giovanni Improta: Department of Public Health, University of Naples “Federico”, 80125 Naples, Italy

IJERPH, 2022, vol. 19, issue 5, 1-9

Abstract: Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of deaths related to birth, different challenges have emerged in identifying the main factors affecting mortality and morbidity. Dataset characterization: We investigated healthcare-associated infections in a cohort of 1203 patients at the level III Neonatal Intensive Care Unit (ICU) of the “Federico II” University Hospital in Naples from 2016 to 2020 (60 months). Methods: The present paper used statistical analyses and logistic regression to identify an association between healthcare-associated blood stream infection (HABSIs) and the available risk factors in neonates and prevent their spread. We designed a supervised approach to predict whether a patient suffered from HABSI using seven different artificial intelligence models. Results: We analyzed a cohort of 1203 patients and found that birthweight and central line catheterization days were the most important predictors of suffering from HABSI. Conclusions: Our statistical analyses showed that birthweight and central line catheterization days were significant predictors of suffering from HABSI. Patients suffering from HABSI had lower gestational age and birthweight, which led to longer hospitalization and umbilical and central line catheterization days than non-HABSI neonates. The predictive analysis achieved the highest Area Under Curve (AUC), accuracy and F1-macro score in the prediction of HABSIs using Logistic Regression (LR) and Multi-layer Perceptron (MLP) models, which better resolved the imbalanced dataset (65 infected and 1038 healthy).

Keywords: statistical analysis; predictive analysis; healthcare-associated infection; healthcare (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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