Building a Novel Artificial Intelligence–Driven Echocardiographic Data Pipeline: Findings From a Large Learning Health System-Reference-Cited by-同舟云学术

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Building a Novel Artificial Intelligence–Driven Echocardiographic Data Pipeline: Findings From a Large Learning Health System

Published:2024-09 Issue:9 Volume:37 Page:916-918
ISSN:0894-7317
Container-title:Journal of the American Society of Echocardiography
language:en
Short-container-title:Journal of the American Society of Echocardiography

Author:

Bose Budhaditya,Butt Sara A.,Arshad Hassan B.,Nicolas Charlie C.,Gullapelli Rakesh,Nwana Nwabunie,Javed Zulqarnain,Shahid Izza,Pournazari Payam,Patel Kershaw,Chamsi Pasha M.A.,Little Stephen H.,Faza Nadeen S.,Jones Stephen,Cainzos M.A.,Al-Kindi Sadeer,Saad Jean Michel,Zoghbi William,Nagueh Sherif F.,Nasir Khurram

Publisher

Elsevier BV

Reference4 articles.

1. Demonstrating the value of outcomes in echocardiography: imaging-based registries in improving patient care;Strom;J Am Soc Echocardiogr,2019

2. Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American society of echocardiography and the European association of cardiovascular imaging;Nagueh;J Am Soc Echocardiogr,2016

3. A natural language processing tool for large-scale data extraction from echocardiography reports;Nath;PLoS One,2016

4. Recommendations for noninvasive evaluation of native valvular regurgitation: a report from the American society of echocardiography developed in collaboration with the society for cardiovascular magnetic resonance;Zoghbi;J Am Soc Echocardiogr,2017

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