Bae Hyojin, Lee Sanghun, Lee Choong-Yeol, Kim Chang-Eop
Department of Physiology, Gachon University College of Korean Medicine, Seongnam, South Korea.
Korean Medicine Data Division, Korea Institute of Oriental Medicine, Daejeon, South Korea.
Front Med (Lausanne). 2022 Feb 3;8:763533. doi: 10.3389/fmed.2021.763533. eCollection 2021.
Pattern identification (PI), a unique diagnostic system of traditional Asian medicine, is the process of inferring the pathological nature or location of lesions based on observed symptoms. Despite its critical role in theory and practice, the information processing principles underlying PI systems are generally unclear. We present a novel framework for comprehending the PI system from a machine learning perspective. After a brief introduction to the dimensionality of the data, we propose that the PI system can be modeled as a dimensionality reduction process and discuss analytical issues that can be addressed using our framework. Our framework promotes a new approach in understanding the underlying mechanisms of the PI process with strong mathematical tools, thereby enriching the explanatory theories of traditional Asian medicine.
证型识别（PI）是传统亚洲医学独特的诊断系统，是基于观察到的症状推断病变的病理性质或位置的过程。尽管其在理论和实践中起着关键作用，但PI系统背后的信息处理原则通常并不明确。我们从机器学习的角度提出了一个理解PI系统的新框架。在简要介绍数据维度之后，我们提出PI系统可以建模为降维过程，并讨论可以使用我们的框架解决的分析问题。我们的框架通过强大的数学工具促进了一种理解PI过程潜在机制的新方法，从而丰富了传统亚洲医学的解释理论。
