DETERMINING POST-TEST RISK IN A SAMPLE OF STRESS NUCLEAR MYOCARDIAL PERFUSION IMAGING REPORTS: IMPLICATIONS FOR NATURAL LANGUAGE PROCESSING

Reporting standards promote clarity and consistency of stress myocardial perfusion imaging (MPI) reports, but do not require an assessment of post-test ischemic risk. Natural Language Processing (NLP) tools could potentially help estimate this risk, yet it is unknown whether reports contain adequate...

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Published in:Journal of the American College of Cardiology 2018-03, Vol.71 (11), p.A1501-A1501
Main Authors: Levy, Andrew, Shah, Nishant, Reeves, Ruth M., Matheny, Michael, Gobbel, Glenn T., Bradley, Steven
Format: Article
Language:English
Subjects:
Cardiology
Medical imaging
Natural language processing
Perfusion
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container_end_page A1501
container_issue 11
container_start_page A1501
container_title Journal of the American College of Cardiology
container_volume 71
creator Levy, Andrew
Shah, Nishant
Reeves, Ruth M.
Matheny, Michael
Gobbel, Glenn T.
Bradley, Steven
description Reporting standards promote clarity and consistency of stress myocardial perfusion imaging (MPI) reports, but do not require an assessment of post-test ischemic risk. Natural Language Processing (NLP) tools could potentially help estimate this risk, yet it is unknown whether reports contain adequate descriptive data to use NLP.
doi_str_mv 10.1016/S0735-1097(18)32042-4
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source BACON - Elsevier - GLOBAL_SCIENCEDIRECT-OPENACCESS
subjects Cardiology
Medical imaging
Natural language processing
Perfusion
title DETERMINING POST-TEST RISK IN A SAMPLE OF STRESS NUCLEAR MYOCARDIAL PERFUSION IMAGING REPORTS: IMPLICATIONS FOR NATURAL LANGUAGE PROCESSING
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