Abstract: Current disease prediction models primarily focus on local and contextual information within medical records,lacking the incorporation of global information,which results in suboptimal prediction accuracy.Leveraging the capability of graph neural networks to capture global information,this study proposes the use of graph convolutional networks (GCN) for tumor disease prediction based on Chinese electronic medical records (EMRs).Firstly,the PKUSEG medical domain-specific word segmentation model is employed to tokenize Chinese EMRs.Then,a text graph is constructed by analyzing the co-occurrence relationships between medical records and words,as well as the relationships between words within the medical text.Finally,the graph convolutional network (Text-GCN) is applied to learn the features of this medical text graph,and the trained model is utilized for tumor disease prediction.Experimental results demonstrate that the proposed model achieves a 6% improvement in accuracy compared to the best-performing baseline model.Moreover,the accuracy does not significantly decline when the dataset is small,indicating that the method exhibits strong robustness even with limited electronic medical records.

Key words: text graph convolutional network, Chinese word segmentation, tumor disease analysis, tumor disease prediction