id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-035216-gdhz7mr4	Li, Xiaoran	Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables	2020-11-06		.txt	text/plain	3780	220	48	title: Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables BACKGROUND: This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. The performance of the DNN model yielded an AUC = 0.780 (95% CI [0.760-0.785]), sensitivity = 0.760, specificity = 0.709 and F1 score = 0.551 in predicting ICU admission for the testing set (Table 2) . Although these variables have been previously associated with COVID-19 infection, most previous studies did not rank these clinical variables, or develop predictive models or risk scores to predict ICU admission or mortality. We implemented a deep-learning algorithm and a risk score model to predict the likelihood of ICU admission and mortality in COVID-19 patients.	./cache/cord-035216-gdhz7mr4.txt	./txt/cord-035216-gdhz7mr4.txt