id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-285897-ahysay2l	Wu, Guangyao	Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study	2020-07-02		.txt	text/plain	3803	178	42	OBJECTIVE: To develop and validate machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. CONCLUSION: The machine-learning model, nomogram, and online-calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Therefore, our objective is to develop and validate a prognostic machine-learning model based on clinical, laboratory, and radiological variables of COVID-19 patients at hospital admission for severity risk assessment during hospitalization, and compare the performance with that of PSI as a representative clinical assessment method. This international multicenter study analyzed individually and in combination, clinical, laboratory and radiological characteristics for COVID-19 patients at hospital admission, to retrospectively develop and prospectively validate a prognostic model and tool to assess the severity of the illness, and its progression, and to compare these with PSI scoring.	./cache/cord-285897-ahysay2l.txt	./txt/cord-285897-ahysay2l.txt