Using machine learning to improve our understanding of COVID-19 infection in children.
PLoS One
; 18(2): e0281666, 2023.
Статья
в английский
| MEDLINE | ID: covidwho-2244260
ABSTRACT
PURPOSE:
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.METHODS:
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.RESULTS:
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.CONCLUSIONS:
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.
Полный текст:
Имеется в наличии
Коллекция:
Международные базы данных
база данных:
MEDLINE
Основная тема:
Pneumonia
/
COVID-19
Тип исследования:
Прогностическое исследование
/
Рандомизированные контролируемые испытания
Пределы темы:
Подростки
/
Взрослые
/
Дети
/
Люди
/
Молодой взрослый
Язык:
английский
Журнал:
PLoS One
Тематика журнала:
Наука
/
Медицина
Год:
2023
Тип:
Статья
Аффилированная страна:
Journal.pone.0281666
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