id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-353499-os328w9o	Yang, H. S.	Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning	2020-06-19		.txt	text/plain	2851	173	55	Here we present a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Overall, this model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-COV-2 infected patients before their RT-PCR results are available. In this study, we hypothesized that the results of routine laboratory tests performed within a short time frame as the RT-PCR testing, in conjunction with a limited number of previously identified predictive demographic factors (age, gender, race) (16), can predict SARS-CoV-2 infection status. selected to construct the input feature vectors of the prediction model based on the following criteria: 1) a result available for at least 30% of the patients two days before a specific SARS-CoV-2 RT-PCR test, and 2) showing a significant difference (p-value, pvalue after Bonferroni correction, p-value after demographics adjustment all less than 0.05) between patients with positive and negative RT-PCR results.	./cache/cord-353499-os328w9o.txt	./txt/cord-353499-os328w9o.txt