https://scholars.lib.ntu.edu.tw/handle/123456789/632588
標題: | Clinical characteristics of hospitalized children with community-acquired pneumonia and respiratory infections: Using machine learning approaches to support pathogen prediction at admission | 作者: | Chang, Tu Hsuan Liu, Yun Chung Lin, Siang Rong Chiu, Pei Hsin CHOU CHIA-CHING LUAN-YIN CHANG FEI-PEI LAI |
關鍵字: | Children | Community-acquired pneumonia | Machine learning | Pathogens prediction | Respiratory infections | 公開日期: | 1-一月-2023 | 來源出版物: | Journal of Microbiology, Immunology and Infection | 摘要: | Background: Acute respiratory infections (ARIs) are common in children. We developed machine learning models to predict pediatric ARI pathogens at admission. Methods: We included hospitalized children with respiratory infections between 2010 and 2018. Clinical features were collected within 24 h of admission to construct models. The outcome of interest was the prediction of 6 common respiratory pathogens, including adenovirus, influenza virus types A and B, parainfluenza virus (PIV), respiratory syncytial virus (RSV), and Mycoplasma pneumoniae (MP). Model performance was estimated using area under the receiver operating characteristic curve (AUROC). Feature importance was measured using Shapley Additive exPlanation (SHAP) values. Results: A total of 12,694 admissions were included. Models trained with 9 features (age, event pattern, fever, C-reactive protein, white blood cell count, platelet count, lymphocyte ratio, peak temperature, peak heart rate) achieved the best performance (AUROC: MP 0.87, 95% CI 0.83–0.90; RSV 0.84, 95% CI 0.82–0.86; adenovirus 0.81, 95% CI 0.77–0.84; influenza A 0.77, 95% CI 0.73–0.80; influenza B 0.70, 95% CI 0.65–0.75; PIV 0.73, 95% CI 0.69–0.77). Age was the most important feature to predict MP, RSV and PIV infections. Event patterns were useful for influenza virus prediction, and C-reactive protein had the highest SHAP value for adenovirus infections. Conclusion: We demonstrate how artificial intelligence can assist clinicians identify potential pathogens associated with pediatric ARIs upon admission. Our models provide explainable results that could help optimize the use of diagnostic testing. Integrating our models into clinical workflows may lead to improved patient outcomes and reduce unnecessary medical costs. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/632588 | ISSN: | 16841182 | DOI: | 10.1016/j.jmii.2023.04.011 |
顯示於: | 生醫電子與資訊學研究所 |
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