基于复合粗糙集的异构属性患者社区划分模型

Community Partition Model of Patients with Heterogeneous Attributes Based on Composite Rough Sets

社区划分是网络研究中的重要组成部分,基于医疗数据对类风湿关节炎患者进行社区划分能够有效提升临床医疗决策的准确性。考虑到社区划分过程中可能会存在患者属性异构及相关性问题,首先基于复合粗糙集理论实现对患者异构属性的有效处理,其次将复合粗糙集理论与louvain算法相融合,构建出基于异构属性节点的社区划分模型。通过采用临床真实数据集及经典网络数据集对本文构建的模型进行实验分析,验证了本文模型能够取得模块值较大的社区结构,实现将不同疾病活动程度的患者划分到不同社区内,从而提升患者疾病活动程度评估的有效性和准确性。

Community partition is an important part of network research.Community partition of patients with Rheumatoid Arthritis based on medical data can effectively improve the accuracy of clinical medical decision-making.Considering that there may be problems of heterogeneity and correlation of patients′attributes in the process of community partition,this paper firstly classifies patients based on composite rough sets theory to effectively deal with heterogeneous attributes.Secondly,the rough sets theory and the louvain algorithm are combined to build a community partition model of patients with heterogeneous attributes.By using the clinical real dataset and the classical network dataset,it is verified that the proposed model can obtain the community structure with large module value,and the realize the division of patients with different disease activity levels into different communities,so as to improve the effectiveness and accuracy of the assessment of patients′disease activity level.