id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-277700-nxm1jr0x	Chassagnon, Guillaume	AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia	2020-04-22		.txt	text/plain	4879	245	49	(i) Two independent cohorts with quantification based on ensemble 2D & 3D consensus neural networks reaching expert-level annotations on massive evaluation, (ii) Consensus-driven bio(imaging)-marker selection on the principle of prevalence across methods leading to variables highly-correlated with outcomes & (iii) Consensus of linear & non-linear classification methods for staging and prognosis reaching optimal performance (minimum discrepancy between training & testing). The approach relied on (i) a disease quantification solution that exploited 2D & 3D convolutional neural networks using an ensemble method, (ii) a biomarker discovery approach sought to determine the share space of features that are the most informative for staging & prognosis, & (iii) an ensemble robust supervised classification method to distinguish patients with severe vs non-severe short-term outcome and among severe patients those intubated and those who did not survive.	./cache/cord-277700-nxm1jr0x.txt	./txt/cord-277700-nxm1jr0x.txt