id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-332180-dw4h69tp	Cheng, Fu-Yuan	Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients	2020-06-01		.txt	text/plain	4124	209	44	We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers' efforts and help hospitals plan their flow of operations. The primary aim of this study is to develop a novel supervised machine learning classifier for predicting the risk of ICU transfer within the next 24 h for COVID-19 patients using hospital EMR data. The following data were retrospectively collected from the Mount Sinai Health System COVID-19 registry, sourced from an EPIC EHR system: demographic information, time-series of the admission-discharge-transfer events, structured and semi-structured clinical assessments, vital signs from nursing flowsheets, and laboratory and electrocardiogram (ECG) results. Using machine learning, we developed a model for identifying deteriorating patients in need of ICU transfer by using data routinely collected during inpatient care. Using machine learning, we developed a model for identifying deteriorating patients in need of ICU transfer by using data routinely collected during inpatient care.	./cache/cord-332180-dw4h69tp.txt	./txt/cord-332180-dw4h69tp.txt