Using machine learning to improve our understanding of COVID-19 infection in children

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.

Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida

“…However, a SHAP global importance plot considered each feature's mean absolute value or weights assigned to the model, over all instances of the current dataset ( 47 – 50 ). The SHAP interpretation, being model-agnostic, provided a means to compute feature importance from the model ( 51 ). It used Shapley values, based on game theory ( 52 ), to estimate how each feature contributed to the prediction ( 49 ).…”