id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-124012-5zxkd2jy	Schwab, Patrick	predCOVID-19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019	2020-05-17		.txt	text/plain	5098	247	38	Here, we study clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test, require hospitalisation or intensive care. In addition, [48] performed a cohort study for clinical and laboratory predictors of COVID-19 related inhospital mortality that identified baseline neutrophil count, age Fig. 2 : The presented multistage machine-learning pipeline consists of preprocessing (light purple) the input data x, developing multiple candidate models using the given dataset (orange), selecting the best candidate model for evaluation (blue), and evaluating the selected best model's outputsŷ. Owing to the recent emergence of SARS-CoV-2, there currently exists, to the best of our knowledge, no prior systematic study on clinical predictive models that predict likelihood of a positive SARS-CoV-2 test, hospital and intensive care unit admission from clinical, demographic and blood analysis data that accounts for the missingness that is characteristic for the clinical setting.	./cache/cord-124012-5zxkd2jy.txt	./txt/cord-124012-5zxkd2jy.txt