Using machine learning to predict bacteremia in febrile children presented to the emergency department

Chih Min Tsai, Chun Hung Richard Lin, Huan Zhang, I. Min Chiu, Chi Yung Cheng, Hong Ren Yu, Ying Hsien Huang*

*此作品的通信作者

研究成果: 期刊稿件文章同行評審

16 引文 斯高帕斯(Scopus)

摘要

Blood culture is frequently used to detect bacteremia in febrile children. However, a high rate of negative or false-positive blood culture results is common at the pediatric emergency department (PED). The aim of this study was to use machine learning to build a model that could predict bacteremia in febrile children. We conducted a retrospective case-control study of febrile children who presented to the PED from 2008 to 2015. We adopted machine learning methods and cost-sensitive learning to establish a predictive model of bacteremia. We enrolled 16,967 febrile children with blood culture tests during the eight-year study period. Only 146 febrile children had true bacteremia, and more than 99% of febrile children had a contaminant or negative blood culture result. The maximum area under the curve of logistic regression and support vector machines to predict bacteremia were 0.768 and 0.832, respectively. Using the predictive model, we can categorize febrile children by risk value into five classes. Class 5 had the highest probability of having bacteremia, while class 1 had no risk. Obtaining blood cultures in febrile children at the PED rarely identifies a causative pathogen. Prediction models can help physicians determine whether patients have bacteremia and may reduce unnecessary expenses.

原文英語
文章編號diagnostics10050307
期刊Diagnostics
10
發行號5
DOIs
出版狀態已出版 - 01 05 2020

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© 2020 by the authors.

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