Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images

Abstract. We have conducted a study of the COVID-19 severity with the chest x-ray images, a private dataset collected from our collaborator St Bernards Medical Center. The dataset is comprised of chest x-ray images from 1,550 patients who were admitted to emergency room (ER) and were all tested positive for COVID-19. Our study is focused on the following two questions: (1) To predict patients hospital staying duration, based on the chest x-ray image which was taken when the patient was admitted to the ER. The length of stay ranged from zero hours to 95 days in the hospital and followed a power law distribution. Based on our testing results, it is hard for the prediction models to detect strong signal from the chest x-ray images. No model was able to perform better than a trivial most-frequent classifier. However, each model was able to outperform the most-frequent classifier when the data was split evenly into four categories. This would suggest that there is signal in the images, and the performance may be further improved by the addition of clinical features as well as increasing the training set. (2) To predict if a patient is COVID-19 positive or not with the chest x-ray image. We also tested the generalizability of training a prediction model on chest x-ray images from one hospital and then testing the model on images captures from other sites. With our private dataset and the COVIDx dataset, the prediction model can achieve a high accuracy of 95.9%. However, for our hold-one-out study of the generalizability of the models trained on chest x-rays, we found that the model performance suffers due to a significant reduction in training samples of any class.