https://scholars.lib.ntu.edu.tw/handle/123456789/597681
標題: | Evaluation of the Need for Intensive Care in Children with Pneumonia: Machine Learning Approach | 作者: | Liu Y.-C. Cheng H.-Y. Chang T.-H. Ho T.-W. Liu T.-C. TING-YU YEN Chou C.-C. LUAN-YIN CHANG CHOU CHIA-CHING FEI-PEI LAI |
關鍵字: | Child pneumonia; Clinical index; Decision making; Intensive care; Machine learning | 公開日期: | 2022 | 出版社: | JMIR Publications Inc. | 卷: | 10 | 期: | 1 | 起(迄)頁: | e28934 | 來源出版物: | JMIR Medical Informatics | 摘要: | Background: Timely decision-making regarding intensive care unit (ICU) admission for children with pneumonia is crucial for a better prognosis. Despite attempts to establish a guideline or triage system for evaluating ICU care needs, no clinically applicable paradigm is available. Objective: The aim of this study was to develop machine learning (ML) algorithms to predict ICU care needs for pediatric pneumonia patients within 24 hours of admission, evaluate their performance, and identify clinical indices for making decisions for pediatric pneumonia patients. Methods: Pneumonia patients admitted to National Taiwan University Hospital from January 2010 to December 2019 aged under 18 years were enrolled. Their underlying diseases, clinical manifestations, and laboratory data at admission were collected. The outcome of interest was ICU transfer within 24 hours of hospitalization. We compared clinically relevant features between early ICU transfer patients and patients without ICU care. ML algorithms were developed to predict ICU admission. The performance of the algorithms was evaluated using sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and average precision. The relative feature importance of the best-performing algorithm was compared with physician-rated feature importance for explainability. Results: A total of 8464 pediatric hospitalizations due to pneumonia were recorded, and 1166 (1166/8464, 13.8%) hospitalized patients were transferred to the ICU within 24 hours. Early ICU transfer patients were younger (P<.001), had higher rates of underlying diseases (eg, cardiovascular, neuropsychological, and congenital anomaly/genetic disorders; P<.001), had abnormal laboratory data, had higher pulse rates (P<.001), had higher breath rates (P<.001), had lower oxygen saturation (P<.001), and had lower peak body temperature (P<.001) at admission than patients without ICU transfer. The random forest (RF) algorithm achieved the best performance (sensitivity 0.94, 95% CI 0.92-0.95; specificity 0.94, 95% CI 0.92-0.95; AUC 0.99, 95% CI 0.98-0.99; and average precision 0.93, 95% CI 0.90-0.96). The lowest systolic blood pressure and presence of cardiovascular and neuropsychological diseases ranked in the top 10 in both RF relative feature importance and clinician judgment. Conclusions: The ML approach could provide a clinically applicable triage algorithm and identify important clinical indices, such as age, underlying diseases, abnormal vital signs, and laboratory data for evaluating the need for intensive care in children with pneumonia. © Yun-Chung Liu, Hao-Yuan Cheng, Tu-Hsuan Chang, Te-Wei Ho, Ting-Chi Liu, Ting-Yu Yen, Chia-Ching Chou, Luan-Yin Chang, Feipei Lai. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 27.01.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124214408&doi=10.2196%2f28934&partnerID=40&md5=2444bf67b5554e6b06fcb609e00b4e3f https://scholars.lib.ntu.edu.tw/handle/123456789/597681 |
ISSN: | 2291-9694 | DOI: | 10.2196/28934 |
顯示於: | 醫學系 |
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