id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-255631-516epnjw	Syeda, H. B.	The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review.	2020-08-25		.txt	text/plain	6751	469	46	Results: The 128 publications selected were classified into three themes based on ML applications employed to combat the COVID-19 crisis: Computational Epidemiology (CE), Early Detection and Diagnosis (EDD), and Disease Progression (DP). This study focused on peer-reviewed publications, as well as, preprints that applied ML techniques to analyze and address COVID-19 crisis on different scales including diagnostics, prognostics, disease spread forecast, omics, and drug development. We identified forty studies that primarily focused on diagnosing COVID-19 in patients with suspected infection mostly using chest radiological images such as Computed Tomography (CT), X-Radiation (X-Ray), and Lung Ultrasound (LUS). In our review, we identified one study by Roy et al [126] who used a deep learning model on annotated LUS COVID-19 dataset to predict disease severity. The goal of the study was to develop a decision support tool that integrates readily available lab results from EHRs. The novel coronavirus (COVID-19) pandemic has strained global healthcare systems, especially ICUs, due to hospitalized patients having higher ICU transfer rates [133] .	./cache/cord-255631-516epnjw.txt	./txt/cord-255631-516epnjw.txt