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Natural language processing to classify electrocardiograms in patients with syncope: A preliminary study.

作者信息

Quinn James, Kim David, Rice Brian Travis, Hao Wei David

机构信息

Department of Emergency Medicine Stanford University California Stanford USA.

出版信息

Health Sci Rep. 2022 Oct 31;5(6):e904. doi: 10.1002/hsr2.904. eCollection 2022 Nov.

Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/297c/9621468/0190dd4cf51e/HSR2-5-e904-g001.jpg

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本文引用的文献

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Emergent cardiac outcomes in patients with normal electrocardiograms in the emergency department.
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Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms.
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Risk Stratification of Older Adults Who Present to the Emergency Department With Syncope: The FAINT Score.
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Comparison of automated interval measurements by widely used algorithms in digital electrocardiographs.
Am Heart J. 2018 Jun;200:1-10. doi: 10.1016/j.ahj.2018.02.014. Epub 2018 Feb 26.
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Predicting Short-term Risk of Arrhythmia among Patients With Syncope: The Canadian Syncope Arrhythmia Risk Score.
Acad Emerg Med. 2017 Nov;24(11):1315-1326. doi: 10.1111/acem.13275. Epub 2017 Oct 12.
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Syncope risk stratification in the ED: directions for future research.
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