摘要
心血管疾病(cardiovascular disease,CVD)在全球范围内造成了巨大的疾病负担,针对CVD实现精确的诊断和评估对于患者预后的改善有着明确的意义。人工智能(artificial intelligence,AI)的出现以及在医学领域的快速应用,使得各类CVD数据的分析和拟合有了新的实现路径。目前CVD领域除了结构化的病历资料外,还包括大量影像、电生理检查带来的非线性数据。如何利用AI来处理此类多源数据目前已有大量的研究进行了探索。因此,该文通过汇总现有各类研究,讨论现有AI技术处理各类多源异构数据的方式,并分析其可能的优势与不足,以期为未来AI在CVD中应用的发展方向提供证据支持。
Cardiovascular disease(CVD)has caused a huge burden of disease worldwide,and accurate diagnosis and assessment of CVD has a clear significance for improving the prognosis of patients.The development of artificial intelligence(AI)and its rapid application in the medical field have enabled new approaches for the analysis and fitting of various CVD data.At present,in addition to structured medical records,the CVD field also includes a large number of non-linear data brought by imaging and electrophysiological examinations.How to use AI to process such multi-source data has been explored by a large number of studies.Therefore,this review discusses the existing ways of processing various multi-source heterogeneous data with existing artificial intelligence technologies by summarizing various existing studies,and analyzes their possible advantages and disadvantages,in order to provide a basis for the future application of AI in CVD.
作者
霍畅
李奕明
HUO Chang;LI Yiming(Department of Cardiology,West China Chunxi Hospital,Sichuan University and the Fourth People’s Hospital of Sichuan Province,Chengdu,Sichuan 610016,P.R.China;Department of Cardiology,West China Hospital,Sichuan University,Chengdu,Sichuan 610041,P.R.China)
出处
《华西医学》
CAS
2023年第5期758-764,共7页
West China Medical Journal
关键词
多源数据
心血管疾病
人工智能
风险模型
Multi-source data
cardiovascular disease
artificial intelligence
risk model
