【学术报告】Optimal transport in echocardiography-based screening for coronary heart disease
报告专家：张静怡 助理教授
工作单位：清华大学统计学研究中心
报告时间：2021年7月19日16:30-17:30
报告地点：数学学院203
邀请人：陈亮
报告摘要：
Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate. We develop an optimal-transport-based image tracing method to analysis echocardiographs, then using a machine learning approach to integrate the extracted echocardiography features and seven clinical features to predict whether one has CHD. We improve the CHD classification accuracy from around 70% to 87.7% on the testing set. The sensitivity of the proposed method is 0.903 and the specificity is 0.843, with an AUC of 0.904, which is significantly higher than those of the individual classification models. Our work lays a foundation for the deployment of speckle tracking echocardiography-based screening tools for coronary heart disease.
专家简介：
张静怡2011年毕业于武汉大学；2013年于武汉大学获得统计学硕士学位；2020年于美国佐治亚大学获得统计学博士学位，师从钟文瑄教授与马平教授。2020年开始在清华大学统计学研究中心担任助理教授。主要研究方向为大数据分析方法和应用，数据融合，去中心化计算和最优传输理论。
