基于多数据源协同学习方法的方剂组配规律研究

项目名称： 基于多数据源协同学习方法的方剂组配规律研究

项目编号： No.61303131

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 林耀进

作者单位： 闽南师范大学

项目金额： 23万元

中文摘要： 深入研究临床有效的方剂，充分挖掘新的药物组配规律，对于降低研发费用、缩短方剂组配研发周期、挖掘新的方剂组配规律具有重要的意义。在对现有药物组配规律研究技术的优缺点进行系统的归纳总结基础上，本项目提出一种基于多数据源协同学习方法发现药物组配规则的计算模型。主要内容：1）从数据源的互补性及冗余性出发，评估关于药物及疾病的海量多源异构描述信息，研究数据源的质量评估与选择方法，以充分有效地获取药物和疾病描述信息。2）分别从信息层面与知识层面出发，利用大间隔及熵约束矩阵分解等策略，引入协同推荐思想，构建多源协作下的药物组配规则发现模型。3）在此基础上，利用药物、方剂的功效信息及疾病描述信息，探讨药物组配后功效值的变化情况，从而挖掘新的药物组配规律。项目的研究思路和方案，将为方剂学提供理论依据，为新药品种研发提供重要的参考，辅助名老中医经验的整理和传承。

中文关键词： 多数据源；协同学习；协同过滤；药物；功效

英文摘要： It is of great significance for the study of prescription matching rule to reduce the research and development cycle of prescription discovery, as well as shorten the related cost, and complete the prescription matching rules. Based on a comprehensive survey of current methods, technology and drawbacks of prescription matching rule, a drug matching rule schema is proposed based on multiple data sources collaborative learning. The study is focused on: 1) We research the quality assessment and selection of data source based on considering the complementarity and redundancy of data source, and evaluate the large-scale biological and pharmological data available for drug and disease, a complete and elaborated representations for these two entites are obtained. 2) From information and knowledge, with strategies such as large magin and matrix decomposition under entropy constraint, we construct a model to find out the drug matching rule under multiple sources cooperation by bringing the idea of collaborative recommend. 3) Based on this, function of drug and prescription, as well as description of diseases, can be applied to research the change of medical effect after distribution, so as to find out a new drug matching rule. The ideology and methodology studied in our project will definitely provide not only a theoreti

英文关键词： multiple data sources；collaborative learning；collaborative filtering；drug；function