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

Autor:	Chi-Yung Cheng, Ying-Hsien Huang, I-Min Chiu, Wun-Huei Zeng, Chun-Hung Richard Lin
Rok vydání:	2021
Předmět:	Scoring system emergency department Logistic regression Machine learning computer.software_genre Article Young infants 03 medical and health sciences 0302 clinical medicine 030225 pediatrics Medicine 030212 general & internal medicine Extreme gradient boosting Ibis biology business.industry young infant fever Retrospective cohort study invasive bacterial infection General Medicine Emergency department biology.organism_classification machine learning Artificial intelligence business computer
Zdroj:	Journal of Clinical Medicine Volume 10 Issue 9 Journal of Clinical Medicine, Vol 10, Iss 1875, p 1875 (2021)
ISSN:	2077-0383
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02a6670d0bd3d5e055dff8ee7507645f https://doi.org/10.3390/jcm10091875 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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