id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-340564-3fu914lk	Cohen, Joseph Paul	Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning	2020-07-28		.txt	text/plain	3257	181	55	In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. In this work, we built and studied a model which predicts the severity of COVID-19 pneumonia, based on CXRs, to be used as an assistive tool when managing patient care. This "pre-training" step was performed on a large set of data in order to construct general representations about lungs and other aspects of CXRs that we would have been unable to achieve on the small set of COVID-19 images available.	./cache/cord-340564-3fu914lk.txt	./txt/cord-340564-3fu914lk.txt