id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-325794-lir8ht2i	Kinar, Y.	Predicting individual risk for COVID19 complications using EMR data	2020-06-05		.txt	text/plain	3215	227	56	the use of an existing EMR-based model for predicting complications due to influenza combined with available epidemiological data to create a model that identifies individuals at high risk to develop complications due to COVID-19 and b. The available dataset for COVID-based model included a total 2137 SARS-CoV-2 positive individuals who were either not hospitalized (n=1658), or hospitalized and marked as mild (n=332), or as having moderate (n=83) or severe (n=64) complications. Here, we describe two approaches and tools to assess the individual risk of developing COVID-19 complications based on medical records: a model developed by combining a machinelearning approach for influenza-like illness (ILI) to be used as a proxy model for COVID-19 and a second model using data on COVID-19 patients. As an initial prior we used the information based on COVID-19 mortality available from China [https://www.worldometers.info/coronavirus/coronavirus-age-sex-demographics/] as proxy for complications probabilities (appendix table 1).	./cache/cord-325794-lir8ht2i.txt	./txt/cord-325794-lir8ht2i.txt