Using machine learning to improve our understanding of COVID-19 infection in children.
Using machine learning to improve our understanding of COVID-19 infection in children.
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DOI：
10.1371/journal.pone.0281666
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发表时间：
2023
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
由于儿童的社会行为，他们感染COVID-19（SARS-CoV-2）的风险增加。这项研究的目的是确定使用放射性胸部X光片是否有助于预测年轻人是否感染COVID-19。本研究共考虑了721名18岁以下个体的2572个胸部印模。一种集成学习方法，随机森林分类器（RFC），用于分类患有感染的患者。使用增量特征实现了五个RFC模型，最好的模型使用所有输入特征实现了0.79的F1分数，ROC曲线下面积为0.85。超参数调整和交叉验证使用网格搜索交叉验证和SHAP模型来确定特征的重要性。发现肺炎、小气道疾病和肺不张（与导管混淆）等放射学特征与预测COVID-19感染状态高度相关。在该样本中，放射性X射线片可以很好地预测COVID-19感染的状况。包括COVID-19测试前后出现的症状的多变量模型产生了良好的预测评分。
Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not. A total of 2572 chest impressions from 721 individuals under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection. Five RFC models were implemented with incremental features and the best model achieved an F1-score of 0.79 with Area Under the ROC curve as 0.85 using all input features. Hyper parameter tuning and cross validation was performed using grid search cross validation and SHAP model was used to determine feature importance. The radiological features such as pneumonia, small airways disease, and atelectasis (confounded with catheter) were found to be highly associated with predicting the status of COVID-19 infection. In this sample, radiological X-ray films can predict the status of COVID-19 infection with good accuracy. The multivariate model including symptoms presented around the time of COVID-19 test yielded good prediction score.
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影响因子：
11.1
作者：
Deer RR;Rock MA;Vasilevsky N;Carmody L;Rando H;Anzalone AJ;Basson MD;Bennett TD;Bergquist T;Boudreau EA;Bramante CT;Byrd JB;Callahan TJ;Chan LE;Chu H;Chute CG;Coleman BD;Davis HE;Gagnier J;Greene CS;Hillegass WB;Kavuluru R;Kimble WD;Koraishy FM;Köhler S;Liang C;Liu F;Liu H;Madhira V;Madlock-Brown CR;Matentzoglu N;Mazzotti DR;McMurry JA;McNair DS;Moffitt RA;Monteith TS;Parker AM;Perry MA;Pfaff E;Reese JT;Saltz J;Schuff RA;Solomonides AE;Solway J;Spratt H;Stein GS;Sule AA;Topaloglu U;Vavougios GD;Wang L;Haendel MA;Robinson PN
通讯作者：
Robinson PN
DOI：
10.1183/16000617.0152-2021
发表时间：
2021-12-31
期刊：
European respiratory review : an official journal of the European Respiratory Society
影响因子：
--
作者：
Adir Y;Saliba W;Beurnier A;Humbert M
通讯作者：
Humbert M
影响因子：
4.5
作者：
Holden TM;Simon MA;Arnold DT;Halloway V;Gerardin J
通讯作者：
Gerardin J
影响因子：
2.3
作者：
Blumfield, Einat;Levin, Terry L.
通讯作者：
Levin, Terry L.
影响因子：
5.9
作者：
Francone, Marco;Iafrate, Franco;Catalano, Carlo
通讯作者：
Catalano, Carlo