id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-306377-s9j21zsy	Yan, Li	A machine learning-based model for survival prediction in patients with severe COVID-19 infection	2020-03-01		.txt	text/plain	3467	208	55	To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 404 infected patients in the region of Wuhan, China to identify crucial predictive biomarkers of disease severity. For this purpose, machine learning tools selected three biomarkers that predict the survival of individual patients with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). https://doi.org/10.1101/2020.02.27.20028027 doi: medRxiv preprint Data Pre-processing: Imported patients' data, used all clinical measurements of their last available date as features and set 'survival' and 'death' as labels for two classes. Multi-tree XGBoost was trained with the parameters setting as the max depth with 4, the learning rate was equal 0.2, the tress number of estimators was set to 150, the value of the regularization parameter α was set to 1 and the 'subsample' and 'colsample_bytree' both were set to 0.9 to prevent overfitting when there were many features but the sample size was not large [5] .	./cache/cord-306377-s9j21zsy.txt	./txt/cord-306377-s9j21zsy.txt