基于判别性子图重构的轻微肝性脑病分类

Minimal Hepatic Encephalopathy Classification Based on Discriminative Subgraph Reconstruction

大量研究表明轻微肝性脑病(MHE)与脑功能网络的异常相关,但难于寻找与MHE相关的异常子网络.为了解决这个问题,文中提出判别性子图重构的方法用于寻找与MHE相关的子网络,并将子网络用于MHE的分类.首先分别从MHE病人和非MHE(NMHE)病人的功能网络中挖掘一组频繁子图.然后,从频繁子图中挑选判别性子图用于重构原网络,并将判别性子图合并用于重构原网络.最后,使用图核计算重构网络之间的相似性,并使用核SVM分类.在包括77位肝硬化病人的数据集上的实验获得较高的分类精度,从而验证方法的有效性.

Minimal hepatic encephalopathy （ MHE） is related to the abnormality of subnetworks, but searching related subnetworks is still a challenging task. To solve this problem, a method based on discriminative subgraph reconstruction is proposed to search subnetworks related to MHE and the subnetworks are used for MHE classification. Firstly, frequent subgraphs are mined from the functional connectivity networks of MHE and non-MHE （NMHE）, respectively. Next, the discriminative subgraphs are selected from the frequent subgraphs for the original networks reconstruction and the combination of discriminative networks is conducted to reconstruct the original networks. Finally, the graph kernel is applied to compute the similarity between pairwise reconstructed networks and the kernel SVM is adopted for MHE classification. On the dataset of 77 patients with hepatic cirrhosis, the high accuracy of the proposed algorithm is achieved and the effectiveness of the proposed method is demonstrated.