Table_1_Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning.docx

Aortic dissection (AD), a dangerous disease threatening to human beings, has a hidden onset and rapid progression and has few effective methods in its early diagnosis. At present, although CT angiography acts as the gold standard on AD diagnosis, it is so expensive and time-consuming that it can hardly offer practical help to patients. Meanwhile, the artificial intelligence technology may provide a cheap but effective approach to building an auxiliary diagnosis model for improving the early AD diagnosis rate by taking advantage of the data of the general conditions of AD patients, such as the data about the basic inspection information. Therefore, this study proposes to hybrid five types of machine learning operators into an integrated diagnosis model, as an auxiliary diagnostic approach, to cooperate with the AD-clinical analysis. To improve the diagnose accuracy, the participating rate of each operator in the proposed model may adjust adaptively according to the result of the data learning. After a set of experimental evaluations, the proposed model, acting as the preliminary AD-discriminant, has reached an accuracy of over 80%, which provides a promising instance for medical colleagues.

主动脉夹层（Aortic dissection, AD）是一种危及人类生命的凶险疾病，其起病隐匿、病情进展迅速，且早期诊断的有效手段匮乏。目前，尽管CT血管造影（CT Angiography）是AD诊断的金标准，但其价格高昂且耗时较长，难以切实为患者提供临床帮助。与此同时，人工智能（Artificial Intelligence, AI）技术可借助AD患者的基础检查信息等常规临床数据，构建低成本且高效的辅助诊断模型，从而提升AD的早期诊断率。因此，本研究拟将五类机器学习算子（Machine Learning Operators）融合为集成诊断模型，作为辅助诊断手段以配合AD临床分析工作。为提升诊断准确率，所提模型中各算子的参与权重可根据数据学习结果自适应调整。经一系列实验评估，该模型作为AD初步判别工具，准确率已达80%以上，可为临床医学同仁提供极具前景的参考范例。