Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score.

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Bibliographic Details
Title: Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score.
Authors: Namasivayam M; Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA mnamasivayam@mgh.harvard.edu., Myers PD; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Guttag JV; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Capoulade R; l'institut du thorax, CHU Nantes, CNRS, INSERM, University of Nantes, Nantes, France., Pibarot P; Cardiology, Quebec Heart and Lung Institute, Laval University, Quebec City, Quebec, Canada., Picard MH; Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA., Hung J; Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA., Stultz CM; Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Source: Open heart [Open Heart] 2022 May; Vol. 9 (1).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, N.I.H., Extramural
Journal Info: Publisher: BMJ Publishing Group Country of Publication: England NLM ID: 101631219 Publication Model: Print Cited Medium: Print ISSN: 2053-3624 (Print) Linking ISSN: 20533624 NLM ISO Abbreviation: Open Heart Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2053-3624
DOI:10.1136/openhrt-2022-001990