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Variational auto-encoders improve explainability over currently employed heatmap methods for deep learning-based interpretation of the electrocardiogram.

作者信息

van de Leur Rutger R, Hassink Rutger J, van Es René

机构信息

Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.

出版信息

Eur Heart J Digit Health. 2022 Oct 26;3(4):502-504. doi: 10.1093/ehjdh/ztac063. eCollection 2022 Dec.

DOI:10.1093/ehjdh/ztac063
PMID:36710900
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9779792/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8ed/9779792/db0a916d7c64/ztac063f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8ed/9779792/db0a916d7c64/ztac063f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8ed/9779792/db0a916d7c64/ztac063f1.jpg

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Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks.基于可解释心电图的深度学习神经网络预测扩张型心肌病患者的致命性室性心律失常。
Europace. 2022 Oct 13;24(10):1645-1654. doi: 10.1093/europace/euac054.
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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.
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Nat Mach Intell. 2019 May;1(5):206-215. doi: 10.1038/s42256-019-0048-x. Epub 2019 May 13.
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The false hope of current approaches to explainable artificial intelligence in health care.当前医疗保健中可解释人工智能方法的虚假希望。
Lancet Digit Health. 2021 Nov;3(11):e745-e750. doi: 10.1016/S2589-7500(21)00208-9.
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Performance of a Convolutional Neural Network and Explainability Technique for 12-Lead Electrocardiogram Interpretation.卷积神经网络性能及 12 导联心电图解释的可解释性技术。
JAMA Cardiol. 2021 Nov 1;6(11):1285-1295. doi: 10.1001/jamacardio.2021.2746.
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