id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-329069-ejdunj41	Yang, He S	Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning	2020-08-21		.txt	text/plain	2361	134	50	METHOD: We developed 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. CONCLUSION: 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) (17), can predict SARS-CoV-2 infection status. Laboratory tests were 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, P-value after Bonferroni correction, P-value after demographics adjustment all less than 0.05) between patients with positive and negative RT-PCR results.	./cache/cord-329069-ejdunj41.txt	./txt/cord-329069-ejdunj41.txt