Department of Computer Science, Sichuan University, Chengdu, Sichuan 610064, PR China.
J Biomed Inform. 2012 Apr;45(2):210-23. doi: 10.1016/j.jbi.2011.10.010. Epub 2011 Nov 10.
Automatic diagnosis is one of the most important parts in the expert system of traditional Chinese medicine (TCM), and in recent years, it has been studied widely. Most of the previous researches are based on well-structured datasets which are manually collected, structured and normalized by TCM experts. However, the obtained results of the former work could not be directly and effectively applied to clinical practice, because the raw free-text clinical records differ a lot from the well-structured datasets. They are unstructured and are denoted by TCM doctors without the support of authoritative editorial board in their routine diagnostic work. Therefore, in this paper, a novel framework of automatic diagnosis of TCM utilizing raw free-text clinical records for clinical practice is proposed and investigated for the first time. A series of appropriate methods are attempted to tackle several challenges in the framework, and the Naïve Bayes classifier and the Support Vector Machine classifier are employed for TCM automatic diagnosis. The framework is analyzed carefully. Its feasibility is validated through evaluating the performance of each module of the framework and its effectiveness is demonstrated based on the precision, recall and F-Measure of automatic diagnosis results.
自动诊断是中医专家系统中最重要的部分之一，近年来受到了广泛的研究。以前的大多数研究都是基于中医专家手动收集、构建和规范化的结构化数据集。然而，由于原始的自由文本临床记录与结构化数据集有很大的不同，因此以前工作的结果不能直接有效地应用于临床实践。它们是非结构化的，并且是中医医生在日常诊断工作中没有权威编辑委员会支持的情况下表示的。因此，本文首次提出并研究了一种利用原始自由文本临床记录进行临床实践的中医自动诊断新框架。尝试了一系列合适的方法来解决框架中的几个挑战，并采用朴素贝叶斯分类器和支持向量机分类器进行中医自动诊断。仔细分析了该框架。通过评估框架每个模块的性能来验证其可行性，并通过自动诊断结果的精度、召回率和 F 度量来证明其有效性。
