标题： 颈动脉狭窄严重程度的深度预测学习 显示英文标题

标题： Deep Predictive Learning of Carotid Stenosis Severity

摘要： 颈动脉狭窄是颈动脉的狭窄，这些动脉为颈部和头部提供血液。 在本工作中，我们训练了一个模型，该模型基于SRUC标准变量和其他患者信息来预测狭窄的严重程度。 我们实现了经典的机器学习方法，决策树和随机森林，在之前的实验中使用过这些方法。 此外，我们通过使用最先进的增强神经ODE深度学习方法提高了准确性。 通过系统且基于理论的分析，我们检查了不同的参数，以达到约77%的准确性。 这些结果表明，最近发展的深度学习方法在应用方面具有强大的潜力，同时表明目前由SRUC标准提供的数据可能不足以在高性能水平上预测狭窄的严重程度。 显示英文摘要

摘要： Carotid artery stenosis is the narrowing of carotid arteries, which supplies blood to the neck and head. In this work, we train a model to predict the severity of the stenosis blockage based on SRUC criteria variables and other patient information. We implement classic machine learning methods, decision trees and random forests, used in a previous experiment. In addition, we improve the accuracy through the use of the state-of-art Augmented Neural ODE deep learning method. Through systematic and theory-rooted analysis, we examine different parameters to achieve an accuracy of about 77%. These results show the strong potential in applying recently developing deep learning methods, while simultaneously suggesting that the current data provided by the SRUC criteria may be insufficient to predict stenosis severity at a high performance level.