id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-336752-cpxnof1b	Zeng, Qi‐Qiang	Radiomics‐based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS‐CoV‐2) and influenza A infected pneumonia	2020-08-13		.txt	text/plain	3366	186	39	Patients were excluded if they met any of the following criteria: (a) history of American Society of Anesthesiologists (ASA) score of more than 2; (b) history of existing respiratory disease prior to outbreak of SARS-CoV-2; (c) pneumonia of etiology other than SARS-CoV-2 or influenza A virus by measuring nucleic acid by fluorogenic quantitative PCR of serum samples and/or oropharyngeal swab samples in conjunction to radiographic evidence and clinically established diagnosis; or (d) absent of obvious pulmonary lesions on radiographic imaging. Abbreviation: ASA score, American society of anesthesiologists score F I G U R E 2 Radiomics-based machine learning workflow, including computed tomography (CT) images acquisition and region-of-interest (ROI) segmentation of inflammatory lesions; radiomic feature extraction by LIFEx; features selection by least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation; radiomics prediction score and calibration; and nomogram development for a more clinicianfriendly application, and support vector machine (SVM) were used to distinguish these two kinds of diseases effectively drawn to compare the predicted outcome versus observed outcome of each patient in order to illustrate the probability of NCP.	./cache/cord-336752-cpxnof1b.txt	./txt/cord-336752-cpxnof1b.txt