Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department

Chiu, I-Min; Cheng, Chi-Yung; Zeng, Wun-Huei; Huang, Ying-Hsien; Lin, Chun-Hung Richard

doi:10.3390/jcm10091875

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Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department

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Chun-Hung Richard Lin

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Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan

Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan

Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan

Author to whom correspondence should be addressed.

Academic Editor: Markus A. Weigand

J. Clin. Med. 2021, 10(9), 1875; https://doi.org/10.3390/jcm10091875

Received: 27 March 2021 / Revised: 19 April 2021 / Accepted: 23 April 2021 / Published: 26 April 2021

(This article belongs to the Special Issue Management of Sepsis and Septic Shock)

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Abstract

Background: The aim of this study was to develop and evaluate a machine learning (ML) model to predict invasive bacterial infections (IBIs) in young febrile infants visiting the emergency department (ED). Methods: This retrospective study was conducted in the EDs of three medical centers across Taiwan from 2011 to 2018. We included patients age in 0–60 days who were visiting the ED with clinical symptoms of fever. We developed three different ML algorithms, including logistic regression (LR), supportive vector machine (SVM), and extreme gradient boosting (XGboost), comparing their performance at predicting IBIs to a previous validated score system (IBI score). Results: During the study period, 4211 patients were included, where 126 (3.1%) had IBI. A total of eight, five, and seven features were used in the LR, SVM, and XGboost through the feature selection process, respectively. The ML models can achieve a better AUROC value when predicting IBIs in young infants compared with the IBI score (LR: 0.85 vs. SVM: 0.84 vs. XGBoost: 0.85 vs. IBI score: 0.70, p-value < 0.001). Using a cost sensitive learning algorithm, all ML models showed better specificity in predicting IBIs at a 90% sensitivity level compared to an IBI score > 2 (LR: 0.59 vs. SVM: 0.60 vs. XGBoost: 0.57 vs. IBI score >2: 0.43, p-value < 0.001). Conclusions: All ML models developed in this study outperformed the traditional scoring system in stratifying low-risk febrile infants after the standardized sensitivity level. View Full-Text

Keywords: machine learning; invasive bacterial infection; young infant fever; emergency department

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MDPI and ACS Style

Chiu, I.-M.; Cheng, C.-Y.; Zeng, W.-H.; Huang, Y.-H.; Lin, C.-H.R. Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department. J. Clin. Med. 2021, 10, 1875. https://doi.org/10.3390/jcm10091875

AMA Style

Chiu I-M, Cheng C-Y, Zeng W-H, Huang Y-H, Lin C-HR. Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department. Journal of Clinical Medicine. 2021; 10(9):1875. https://doi.org/10.3390/jcm10091875

Chicago/Turabian Style

Chiu, I-Min, Chi-Yung Cheng, Wun-Huei Zeng, Ying-Hsien Huang, and Chun-Hung R. Lin. 2021. "Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department" Journal of Clinical Medicine 10, no. 9: 1875. https://doi.org/10.3390/jcm10091875

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