• Felipe Yu Matsushita, KrebsVera Lúcia JornadaVLJDepartment of Pediatrics, Neonatology Division, Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, Brazil., and Werther Brunow de Carvalho.
• Department of Pediatrics, Neonatology Division, Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, Brazil. Electronic address: felipe.matsushita@hc.fm.usp.br.
• Clinics (Sao Paulo). 2023 Jan 1; 78: 100148100148.

PurposeThe authors aimed to develop a Machine-Learning (ML) algorithm that can predict positive blood culture in the neonatal intensive care unit, using complete blood count and C-reactive protein values.MethodsThe study was based on patients' electronic health records at a tertiary neonatal intensive care unit in São Paulo, Brazil. All blood cultures that had paired complete blood count and C-reactive protein measurements taken at the same time were included. To evaluate the machine learning model's performance, the authors used accuracy, Area Under the Receiver Operating Characteristics (AUROC), recall, precision, and F1-score.ResultsThe dataset included 1181 blood cultures with paired complete blood count plus c-reactive protein and 1911 blood cultures with paired complete blood count only. The f1-score ranged from 0.14 to 0.43, recall ranged from 0.08 to 0.59, precision ranged from 0.29 to 1.00, and accuracy ranged from 0.688 to 0.864.ConclusionComplete blood count parameters and C-reactive protein levels cannot be used in ML models to predict bacteremia in newborns.Copyright © 2022 HCFMUSP. Published by Elsevier España, S.L.U. All rights reserved.

14 keywords...

hide…