融合粗糙集与GRA的异构信息多准则三支推荐及其在医疗推荐中的应用

在临床实践中,医疗推荐可能存在数据多源异构和推荐项目多准则的问题,考虑到医疗推荐的这些特征,定义了异构信息系统上不同数据类型的距离测度,实现多源异构数据的有效处理.首先,根据两个对象之间的混合距离得到异构信息系统中的二元关系,并构建异构信息粗糙集模型;然后,将多准则推荐与多准则决策方法(MCDM)相结合,运用灰色关联分析(GRA)聚合每个项目下多准则评分将其转化为单评分推荐;最后,在异构信息粗糙集模型的基础上引入三支决策,同时基于协同过滤方法实现三支推荐,考虑了推荐过程中的决策成本.在医疗应用部分采用临床数据实验,验证了所提出的模型能够为临床诊断提供知识支持,有效降低推荐决策成本,提高推荐的准确性.

Multi-criteria three-way recommendation of heterogeneous information based on rough set and GRA and its application in medical recommendation

Medical recommendation may have the problems of multi-source heterogeneous data and multi-criteria of recommendation items in clinical practice. Considering the characteristics of medical recommendation, this paper defines the distance measure of different types of data in heterogeneous information systems and the effective processing of multi-source heterogeneous data is realized. Firstly, meanwhile the binary relationship in heterogeneous information systems is obtained according to the hybrid distance between two objects and the heterogeneous information rough set model is constructed subsequently. Then the multi-criteria recommendation is combined with the multi-criteria decision-making method(MCDM). And the multi-criteria rating of each item is aggregated by grey relational analysis(GRA) to transform multi-criteria recommendation into single-rating recommendation. Finally, three-way decision-making is presented on the basis of the heterogeneous information rough set model, and three-way recommendation is achieved based on the collaborative filtering method, which considers the cost of decision-making in the process of recommendation. Clinical practice data in the part of medical application is used to verify that the proposed model can provide a knowledge support for clinical diagnosis, which effectively reduces the cost of recommendation and improves the accuracy of recommendation.