Department of Computer Science and Informatics Institute, University of Missouri, Columbia, MO 65211, USA.
Evid Based Complement Alternat Med. 2012;2012:912852. doi: 10.1155/2012/912852. Epub 2012 May 31.
ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.
中医证候是中医理论的一个整体和重要组成部分。它定义了个体患者症状特征的理论抽象，因此被用作中医疾病分类的指南。例如，患有胃炎的患者可能被归类为寒证或热证，而患有不同疾病的患者可能被归类为同一证型。舌象是判断患者证型的一种有价值的诊断工具。在本文中，我们探索了使用各种监督机器学习算法对证型进行临床特征描述的新方法。我们提出了一种基于新颜色空间的特征集，可以从临床患者的舌象图像中提取出来，以构建一个自动证型分类系统。鉴于中医医生通常通过观察舌色和舌苔来确定证型，并诊断包括胃炎在内的不同胃部疾病，我们提出利用机器学习技术通过示例学习来建立舌象特征与证型之间的关系。在一组 263 名胃炎患者和 48 名健康志愿者的实验结果中，我们的系统表现出色，其中大多数患者患有寒证或热证。
