基于人工智能的集群算法用于脉冲诊断
Junsuk Kim1, Won-Joon Koh2, Heeyoung Moon3
1School of Information Convergence, Kwangwoon University, Seoul, Republic of Korea.
Journal of multidisciplinary healthcare
|December 10, 2025
概括
一个用于脉冲诊断的AI算法显示了与专家诊断的高度对齐,特别是对于"浮沉"模式. 这种方法旨在使用客观,数据驱动的方法来标准化传统的脉冲诊断.
科学领域:
- 综合医学是一个整体的医学.
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 传统的脉冲测试是一种主观的诊断技术,在可靠性和客观性方面面临挑战.
- 人工智能 (AI) 提供了对生物信号的客观分析的潜力.
- 开发人工智能驱动的工具可以提高传统诊断方法的科学严谨性.
研究的目的:
- 开发和验证基于人工智能的算法,用于集群脉冲波形信号.
- 客观地评估传统医学从业者发现的诊断模式.
- 探索AI在标准化传统脉冲诊断方面的潜力.
主要方法:
- 从健康个体的两个手腕收集脉冲信号.
- 在脉冲波形数据上使用无监督的集群技术.
- 利用动态时间变形 (DTW) 来实现脉冲相似性和多维缩放 (MDS) 来减少维度.
主要成果:
- 人工智能算法与专家诊断高度一致,有效地集群数据驱动的模式.
- 这是一个很棒的节目,这是一个很棒的节目.
- 漂浮-沉没的漂浮方式
- 脉冲模式显示了最高的Cosine相似性 (0.83).
- 来自左手腕的脉冲信号与右手腕 (0.54 ± 0.15) 相比,显示出稍微更好的对齐 (0.56 ± 0.13).
结论:
- 人工智能驱动的模式识别显示了与传统脉冲诊断中的专家诊断的显著对齐.
- 开发的算法为标准化和量化主观诊断技术提供了一条途径.
- 建议对多样化的群体进行进一步的研究,以改进人工智能诊断工具,以便更广泛的临床应用.
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