Research On Symptoms And Pathogenesis Recognition Of Traditional Chinese Medicine And Its Relations

The case of Traditional Chinese Medicine contains rich knowledge. As the carrier of Chinese medicine theory, it guides clinical practice. So it's important to develop of the medical cases and make full use of it.The main work of this paper is study the medical symptoms and pathogenesis which is in the medical case of the Ming and Qing dynasties. We use of natural language processing knowledge of corpus linguistics and statistical methods based on the named entity recognition technology, at last we use SimRank which is a lately technology of data mining. Specifically, the main task of this paper reads as follows:First of all, we built a rule-based Chinese medicine Medical Corpus. We have analyzed a large number of medical documentation, We summarized the language of the general characteristics of the Traditional Chinese Medicine cases. Combination of these features we propose a multi-level medical corpus.Secondly, in view of the general characteristics of the Traditional Chinese Medicine cases, We propose some methods which are data cleaning, feature templates, feature select and how to reduce the feature space. Then we use statistical methods based on the named entity recognition technology to recognize symptoms and pathogenesis. Recognition results show that our method is applicable.At last, we study the multi-classification of chronic gastritis. We use SimRank to extract the relation of symptoms and pathogenesis which is in the case of chronic gastritis. We calculate the similarity of pathogenesis with its relationship-related symptoms.