Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records-Reference-Cited by-同舟云学术

Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records

Published:2024 Issue: Volume:44 Page:101418
ISSN:2352-9148
Container-title:Informatics in Medicine Unlocked
language:en
Short-container-title:Informatics in Medicine Unlocked

Author:

Alskaf Ebraham^ORCID,Frey Simon M.,Scannell Cian M.,Suinesiaputra Avan,Vilic Dijana,Dinu Vlad,Masci Pier Giorgio,Perera Divaka,Young Alistair,Chiribiri Amedeo

Funder

Wellcome Trust

Siemens Healthineers

NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London

Wellcom/EPSRC Centre for Medical Engineering

Publisher

Elsevier BV

Subject

Health Informatics

Reference21 articles.

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3. Magnetic resonance first-pass myocardial perfusion imaging: clinical validation and future applications;Wilke;J Magn Reson Imag,1999

4. Noninvasive detection of myocardial ischemia from perfusion reserve based on cardiovascular magnetic resonance;Al-Saadi;Circulation,2000

5. Effect of care guided by cardiovascular magnetic resonance, myocardial perfusion scintigraphy, or NICE guidelines on subsequent unnecessary angiography rates: the CE-MARC 2 randomized clinical trial;Greenwood;JAMA,2016

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