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Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency
I-Min Chiu1,2, Chi-Yung Cheng1,2, Wun-Huei Zeng2
1Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan.
Insights
Machine learning models accurately predict invasive bacterial infections (IBIs) in febrile infants. These models outperform traditional scoring systems, improving diagnostic accuracy for young children in emergency departments.
Area of Science:
- Pediatric Emergency Medicine
- Clinical Informatics
- Machine Learning in Healthcare
Background:
- Young febrile infants presenting to the emergency department (ED) require accurate diagnosis of invasive bacterial infections (IBIs).
- Traditional scoring systems may have limitations in accurately stratifying risk for IBIs in this vulnerable population.
Purpose of the Study:
- To develop and evaluate machine learning (ML) models for predicting IBIs in young febrile infants.
- To compare the performance of ML models against a validated IBI score system.
Main Methods:
- Retrospective study in 3 Taiwanese EDs (2011-2018) including infants aged 0-60 days with fever.
- Development and comparison of three ML algorithms: logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGBoost).
- Feature selection was performed for each ML model.
Main Results:
- Out of 4211 infants, 3.1% had IBI. ML models demonstrated superior predictive performance compared to the IBI score (AUROC: 0.84-0.85 vs. 0.70, p < 0.001).
- At 90% sensitivity, ML models achieved significantly higher specificity in predicting IBIs (0.57-0.60) than the IBI score > 2 (0.43, p < 0.001).
Conclusions:
- Machine learning models significantly outperform the traditional IBI score in predicting invasive bacterial infections in young febrile infants.
- The developed ML models offer a more accurate tool for risk stratification, potentially improving clinical decision-making in the ED.
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.
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