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机器学习方法用于心血管疾病预测:一篇综述

Siming Wan¹, Feng Wan², Xi-Jian Dai³

¹Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, 330006 Nanchang, China; Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, 330006 Nanchang, China; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, China.

Archives of cardiovascular diseases

|June 14, 2025

概括

机器学习算法显著改善了早期心血管疾病的诊断. 这篇评论分析了人工智能在心脏健康中的算法,实施和伦理考虑.

科学领域:

人工智能在医学中的应用
心血管疾病的诊断心血管疾病的诊断
机器学习应用机器学习应用

背景情况:

心血管疾病 (CVD) 仍然是全球死亡和发病的主要原因.
传统的诊断方法在早期发现疾病方面存在局限性.
人工智能 (AI),特别是机器学习 (ML),提供了先进的诊断功能.

研究的目的:

为早期心血管疾病诊断进行ML算法的系统审查.
分析将ML纳入临床实践的操作框架.
批判性地评估监管合规性和AI在心脏病学中的伦理影响.

主要方法:

系统审查最近关于ML的文献,以预测心血管疾病.
分析不同ML算法的比较性能.
检查临床实施工作流程和监管框架.

主要成果:

与传统方法相比,ML算法在早期心血管疾病诊断中表现出更高的准确性.
确定了用于成功临床整合机器学习工具的关键操作框架.
突出了需要仔细考虑的重大监管和道德挑战.

结论:

ML在早期心血管疾病检测和管理方面具有变革性的潜力.
成功的临床实施需要强大的框架来解决运营,监管和伦理方面的问题.
未来的研究应该专注于改进算法和解决实施障碍.

关键词:

心血管疾病是什么心血管疾病分类分类分类分类.功能选择功能选择机器学习机器学习预测预测预测

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