应用机器学习算法通过全血细胞计数和 C 反应蛋白预测新生儿血培养阳性。
Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms.
机构信息
Department of Pediatrics, Neonatology Division, Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, Brazil.
Department of Pediatrics, Neonatology Division, Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, Brazil.
出版信息
Clinics (Sao Paulo). 2022 Dec 8;78:100148. doi: 10.1016/j.clinsp.2022.100148. eCollection 2023.
PURPOSE
The 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.
METHODS
The 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.
RESULTS
The 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.
CONCLUSION
Complete blood count parameters and C-reactive protein levels cannot be used in ML models to predict bacteremia in newborns.
目的
作者旨在开发一种机器学习（ML）算法，使用全血细胞计数和 C 反应蛋白值来预测新生儿重症监护病房中的阳性血培养。
方法
本研究基于巴西圣保罗一家三级新生儿重症监护病房的患者电子健康记录。所有血培养均同时进行了配对的全血细胞计数和 C 反应蛋白测量。为了评估机器学习模型的性能，作者使用了准确性、接收者操作特征曲线下的面积（AUROC）、召回率、精度和 F1 分数。
结果
数据集包括 1181 份配对的全血细胞计数加 C 反应蛋白血培养和 1911 份配对的全血细胞计数血培养。F1 分数范围为 0.14 至 0.43，召回率范围为 0.08 至 0.59，精度范围为 0.29 至 1.00，准确性范围为 0.688 至 0.864。
结论
全血细胞计数参数和 C 反应蛋白水平不能用于 ML 模型来预测新生儿菌血症。
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