基于血管造影的机器学习评估心肌缺血：开发与回顾性验证数据集

资源简介

基于血管造影的机器学习评估心肌缺血：开发与回顾性验证数据集

【数据集背景】

本数据集源站标题为《Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation》。配套论文《Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation》发表于 PLOS Medicine（2018），DOI：10.1371/journal.pmed.1002693。源站首次发布：2018-11-13。

【数据内容】

源站原始描述：Background Invasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%–65%) for the prediction of FFR Methods and findings A retrospective study was conducted using data from 1,132 stable and unstable angina patients with 1,132 intermediate lesions who underwent invasive coronary angiogra…

【数据结构与技术规格】

文件数 11 个，合计 15.30 MB。pmed.1002693.s001.pdf：application/pdf，670.76 KB。pmed.1002693.s003.doc：application/msword，107.01 KB。数据集 DOI：10.1371/journal.pmed.1002693。

【主题与分类】

源站学科分类：Space Science、Medicine、Biotechnology、Biological Sciences not elsewhere classified。主题标签：CHA、support vector machine、retrospective validation Background Invasive、LAD、FFR、MAE、Asan BiomedicaL Research Environment、angiography、CCTA、sub。

【适用方向】

源站学科分类标注为 Space Science、Medicine、Biotechnology、Biological Sciences not elsewhere classified。

【获取与许可】

源站页面：https://plos.figshare.com/articles/dataset/Machine_learning_assessment_of_myocardial_ischemia_using_angiography_Development_and_retrospective_validation/7335521

使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。

规范引用：Hae, Hyeonyong; Kang, Soo-Jin; Kim, Won-Jang; Choi, So-Yeon; Lee, June-Goo; Bae, Youngoh; et al. (2018). Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation. PLOS Medicine. Dataset

【使用注意】

· 源站最后修改于 2023-06-02，引用时请注意版本时效。