id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-204125-fvd6d44c	Chowdhury, Muhammad E. H.	An early warning tool for predicting mortality risk of COVID-19 patients using machine learning	2020-07-29		.txt	text/plain	3883	199	47	Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high sensitive C-reactive protein, and age acquired at hospital admission were identified as key predictors of death by multi-tree XGBoost model. The prognostic model, nomogram and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification. [21] reported a machine learning approach to select three biomarkers (lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP)) and using them to predict individual patients mortality, 10 days ahead with more than 90 percent accuracy. Although several predictive prognostic models are proposed for the early detection of individuals at high risk of COVID-19 mortality, a major gap remains in the design of state-of-the-art interpretable machine learning based algorithms and high performance quantitative scoring system to classify the most selective predictive biomarkers of patient death.	./cache/cord-204125-fvd6d44c.txt	./txt/cord-204125-fvd6d44c.txt