The research of medical diagnosis based on LDA model

中医作为传统生命科学的一个重要组成部分，在疾病诊疗方面具有特色和显著的临床疗效。几千年的中医诊疗实践积累大量的临床数据和医学文献，这些数据包含宝贵的中医理论知识和规律，利用数据挖掘技术探求中医药诊治疾病的规律，形成用数字描述和表达的中医药内容，将有力推动中医药研究的规范化进程。近年来，研究人员应用聚类分析、关联规则和回归分析等方法研究中医理论，并已取得一定的研究进展，但由于中医药信息的特殊性，对挖掘算法的高效性和鲁棒性有较高的要求，仍难以体现中医语义复杂性特点及中医诊疗系统性特点。 本文利用主题模型研究中医临床诊疗规律，我们不仅认为主题模型能够提取中医临床诊疗数据的语义特征，而且关于主题模型...traditional Chinese medicine as an important part in life science has significant characteristics and clinical curative effects on the diagnosis and treatment of diseases. Thousands of years of Chinese medicine diagnosis and treatment practice has accumulated numerous clinical data, these data contains precious Chinese medicine theory knowledge and law, using data mining technology to search the l...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机应用技术学号：2302009115276

基于领域本体的中医语义推理诊断系统

本文基于本体理论建立中医系统知识库,并在此基础上开发智能诊断系统。为了在诊断推理中与用户输入的症状相匹配,文中采用统计学中的TfIdf结合语义思想的方法进行相似度计算排序解决,该系统为中医临床医生提供一个诊疗决策的优良工具

Web Log Data Based on Potential Lejeune Dirichlet Allocation Model

近年来,基于WEb日志的数据挖掘技术逐渐成为理论研究和商业应用中的热点问题,而其中WEb用户分类又是挖掘领域中最重要的研究主题之一.对WEb用户分类能够发现用户之间相似的用户行为,从而针对具体用户群设置对应的服务项目.根据用户的历史访问网页地址(url)信息,提出了基于加权潜在狄利克雷分配(ldA)模型的用户分类方法,将用户划分到不同的主题群体,实验表明,这种方法能达到很好的分类效果.In recent years,the data mining technology based on the Web log is becoming a hot issue in the theoretical research and commercial applications,and the classification of Web users is one of the most important research in the field of data mining.Classification of Web users can help us find the similarities between the behavior of users,so as to set up the corresponding service project for specific user group.In this paper,according to the history information of the URL which users had accessed,we proposed a user classification method based on weighted potential Lejeune Dirichlet allocation(LDA) model,and the user is divided into different topic groups.Experiments show that this method can achieve very good classification results

The Research of Medical Data Based on Potential Lejeune Dirichlet Allocation Model

以潜在狄利克雷分配(lEJEunE dIrICHlET AllOCATIOn,ldA)模型为基础,研究中医诊疗中的多关系主题模型,提出一个症状-中药-治疗-诊断方法(SyMPTOM-HErb-THErAPIES-dIAgnOSIS TOPIC,SHTdT)模型,用于提取中医临床数据中的症状、中药、治疗方法和诊断的主题结构.参数推理采用gIbbS抽样,根据主题间平均相似度,确定最佳主题数.实验中采用SHTdT模型可以预测给定症状的患者的主题分布、中药、治疗方法及诊断结果,为临床医生和研究人员提供参考.结果表明该模型能够为中医临床诊疗规律的研究提供一个新的统计工具.Using potential Lejeune Dirichlet allocation(LDA) model as the foundation,we study the more relationship-theme-model in Chinese medicine diagnosis and treatment and put forward the Symptom-Herb-Therapies-Diagnosis Topic(SHTDT) model.Parameters reasoning use Gibbs sampling method to confirm the best number of topics according to the average similarity between themes.The SHTDT model can predict the distribution of themes of the patient who gives his symptoms,and forecast his Chinese medicine、treatment and diagnosis.The model provides the reference for clinical doctors and researchers.The results show that the model can provide a new statistical tool for the rule of Chinese medicine clinical diagnosis and treatment