PREDICTION OF CLINICAL OUTCOME IN AORTIC STENOSIS USING MACHINE LEARNING

Autor:	Paul R. Myers, Judy Hung, Collin M. Stultz, Mayooran Namasivayam, John V. Guttag, Michael H. Picard
Rok vydání:	2020
Předmět:	Stenosis Lasso regression business.industry Medicine Low gradient Artificial intelligence Cardiology and Cardiovascular Medicine Machine learning computer.software_genre business medicine.disease computer Outcome (game theory)
Zdroj:	Journal of the American College of Cardiology. 75:2181
ISSN:	0735-1097
DOI:	10.1016/s0735-1097(20)32808-4
Popis:	Predicting outcomes in aortic stenosis (AS) is challenging, especially in low gradient AS (LGAS). Machine learning (ML) can identify important outcome predictors. In 1,130 patients with moderate or severe AS, we used bootstrap lasso regression (BLR), an ML tool, to identify echocardiographic and
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e6a0a0d33e113ebfaa07e9a07cd2e85e https://doi.org/10.1016/s0735-1097(20)32808-4 Zobrazit plný text záznamu