Medical Graph Diffusion: Hybrid Graph Diffusion With Heterogeneous Graph Convolutional Networks for Medical Text Classification
Guishen Wang, Shengnan Li, K. Y. Liu, Xiaowen Hu, Chen Cao
Abstract
Text classification is a critical task for understanding the knowledge behind text, especially in medical text. In this paper, we propose a medical graph diffusion model, named the MGD model, for the medical text classification task. To model more structural relationships within a document, our MGD model constructs a text heterogeneous graph to represent word-level, sentence-level, and word-sentence-level structural relationships. To overcome the limitation of only considering direct neighbors, a graph diffusion convolution is employed to reconstruct the text heterogeneous graph. Subsequently, a heterogeneous graph convolutional network and a multilayer perceptron are used to complete the medical text classification task. To evaluate the performance of our MGD model, various text classification benchmarks, including long text standard benchmarks, short text standard benchmarks, and medical text benchmarks, are used to comprehensively assess the effectiveness and robustness of our MGD model. Compared with other representative baselines, it achieved notable improvements in both Accuracy and F1 score evaluation metrics. Ablation experiment results further demonstrated that the construction of heterogeneous graphs and the use of diffusion graph convolutional networks significantly impact the performance of our MGD model.