id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-269914-75to9xr2	Jansson, Miia	Artificial Intelligence for clinical decision support in Critical Care, required and accelerated by COVID-19	2020-10-21		.txt	text/plain	1363	76	30	Diagnostic models have been proposed in a variety of clinical situations including early detection or stratification of sepsis [5] , bacterial and viral infections (e.g., COVID-19) [5] , and delirium in the ICU [5] , as well as pulmonary embolism in primary care [6] . Prognostic models have focused on predicting ICU-related mortality [7] , infections (e.g., positive blood culture, MRSA) [5] , responses to treatments [5] , antibiotic resistance [5] , asynchronies during assisted ventilation [8] , prolonged MV [9] , extubation failure [10] , and death in influenza [11] , COVID-19 [12, 13] , and community-acquired pneumonia [14] . Geolocated critical care demand prediction, optimal hospital resource planning, and intelligent patient flow management with decision support algorithms can also be achieved by integrating real time clinical data with population statistics and health interventions.	./cache/cord-269914-75to9xr2.txt	./txt/cord-269914-75to9xr2.txt