Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Comput Methods Programs Biomed. 2019 Jan;168:39-57. doi: 10.1016/j.cmpb.2018.10.017. Epub 2018 Oct 27.
Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of the application of data mining methods in traditional medicine.
We reviewed 5 database between 2000 to 2017 based on the Kitchenham systematic review methodology. 502 articles were identified and reviewed for their relevance to application of machine learning methods in traditional medicine, 42 selected papers were classified and categorized on four dimension; 1) application domain of data mining techniques in traditional medicine; 2) the data mining methods most frequently used in traditional medicine; 3) main strength and limitation of data mining techniques in traditional medicine; 4) the performance evaluation methods in data mining methods in traditional medicine.
The result obtained showed that main application domain of data mining techniques in traditional medicine was related to syndrome differentiation. Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) were recognized as being the methods most frequently applied in traditional medicine. Furthermore, each data mining techniques has its own strength and limitations when applied in traditional medicine. Single scaler methods were frequently used for performance evaluation of data mining methods.
Machine learning methods have become an important research field in traditional medicine. Our research provides information about this methods by examining the related articles.
尽管机器学习方法在传统医学中的应用非常重要，但目前尚无针对该领域的系统文献综述和分类。这是首次对数据挖掘方法在传统医学中的应用进行全面的文献综述。
我们根据 Kitchenham 系统综述方法，对 2000 年至 2017 年间的 5 个数据库进行了回顾。共确定了 502 篇文章，并对其与机器学习方法在传统医学中的应用相关性进行了回顾，有 42 篇选定的论文在四个维度上进行了分类和归类：1）数据挖掘技术在传统医学中的应用领域；2）传统医学中最常用的数据挖掘方法；3）数据挖掘技术在传统医学中的主要优势和局限性；4）传统医学中数据挖掘方法的性能评估方法。
结果表明，数据挖掘技术在传统医学中的主要应用领域与证候分类有关。贝叶斯网络（BNs）、人工神经网络（ANNs）和支持向量机（SVMs）被认为是传统医学中最常用的方法。此外，每种数据挖掘技术在应用于传统医学时都有其自身的优势和局限性。单一指标方法常用于数据挖掘方法的性能评估。
机器学习方法已成为传统医学中的一个重要研究领域。我们的研究通过检查相关文章，提供了有关这些方法的信息。
