id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-325170-50oy9qqy	Bai, Xiang	Predicting COVID-19 malignant progression with AI techniques	2020-03-23		.txt	text/plain	3043	172	51	Specifically, we employed a deep learning-based model to effectively mine the complementary information in static clinical data and serial quantitative chest CT sequence. The differences of clinical and laboratory data and imaging features between the patient with and without severe/critical progression were compared using Chi-square test, Fisher's exact test, independent t test and paired t test. With the worldwide outbreak of COVID-19, early prediction and early aggressive treatment of mild patients at high risk of malignant progression to severe/critical stage are important ways to reduce mortality. We also demonstrated that our method can effectively fuse these two complementary data and handle time-series information in the quantitative chest CT sequence, which achieved an AUC of 0.954 (95% CI Although lots of clinical, laboratory, and imaging parameters varied significantly between patients with and without severe/critical progression, seven predictive All rights reserved. In conclusion, the deep learning-based method using clinical and quantitative CT data to predict malignant progression to severe/critical stage.	./cache/cord-325170-50oy9qqy.txt	./txt/cord-325170-50oy9qqy.txt