id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-335061-wn8u7u9y	Zheng, Yichao	A Learning-based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics	2020-08-03		.txt	text/plain	3463	200	51	This model is found effective to identify severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. In light of this unmet need in efficient triage of COVID-19 cases, the study is sought to 56 develop and validate a learning-based model that evaluates patients' priority of being 57 admitted to hospital care due to their appearance or susceptibility toward severe 58 COVID-19. As this study was sought to identify 86 the hospitalization priority according to the prehospital assessment of severe COVID-19 87 risk, only clinical data obtained on admission were used to evaluate the importance of 88 clinical variables in identification of severe or potentially severe cases. To assess the 358 effectiveness of models in early prediction of severe progressions, patients who were 359 presented with non-severe symptom on admission but developed severe disease during 360 hospitalization were enrolled as an external testing set for analysis.	./cache/cord-335061-wn8u7u9y.txt	./txt/cord-335061-wn8u7u9y.txt