Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC⋅BarcelonaTech), 08034 Barcelona, Spain.
Department of Physics, Universitat Politècnica de Catalunya (UPC⋅BarcelonaTech), 08028 Barcelona, Spain.
Viruses. 2021 Dec 30;14(1):63. doi: 10.3390/v14010063.
Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms.
Epidemiological and clinical data were obtained from the REDCap registry. Overall, 4434 SARS-CoV-2 tests were performed in symptomatic children between 1 November 2020 and 31 March 2021, 784 were positive (17.68%). We pre-processed the data to be suitable for a machine learning (ML) algorithm, balancing the positive-negative rate and preparing subsets of data by age. We trained several models and chose those with the best performance for each subset.
The use of ML demonstrated an AUROC of 0.65 to predict a COVID-19 diagnosis in children. The absence of high-grade fever was the major predictor of COVID-19 in younger children, whereas loss of taste or smell was the most determinant symptom in older children.
Although the accuracy of the models was lower than expected, they can be used to provide a diagnosis when epidemiological data on the risk of exposure to COVID-19 is unknown.
检测严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)感染在儿童中既不可行也不容易。我们旨在提出一种机器学习模型,根据儿童(<16 岁)的临床症状评估是否需要进行 SARS-CoV-2 检测。
从 REDCap 注册表中获取流行病学和临床数据。2020 年 11 月 1 日至 2021 年 3 月 31 日期间,对有症状的儿童共进行了 4434 次 SARS-CoV-2 检测,其中 784 次呈阳性(17.68%)。我们对数据进行预处理,使其适合机器学习(ML)算法,平衡阳性与阴性率,并按年龄准备数据子集。我们训练了多个模型,并为每个子集选择性能最佳的模型。
使用 ML 预测儿童 COVID-19 诊断的 AUROC 为 0.65。无高热是儿童 COVID-19 的主要预测因素,而味觉或嗅觉丧失是大龄儿童最具决定性的症状。
尽管模型的准确性低于预期,但当 COVID-19 暴露风险的流行病学数据未知时,它们可以用于提供诊断。