Patient-Oriented Herb Recommendation System Based on Multi-Graph Convolutional Network

Abstract

The presented herb recommendation system aims to analyze the patients’ symptoms and recommends a set of herbs as the prescription to treat diseases. In addition to symptoms, the patients’ personal properties and induced diagnoses are also essential for treatment making. Specifically, for different age groups, the treatments are different. However, the existing studies only use symptoms to represent patients and ignore the patients’ multidimensional features modeling. Thus, these models are insufficiently personalized. Meanwhile, most of these existing herb recommendation models based on graphs have not distinguished the effects of different node types. To address the above limitations, we propose a model named Patient-Oriented Multi-Graph Convolutional Network-based Herb Recommendation system (PMGCN). The prediction model contains two effective modules, patient portraits modeling and herb interactions modeling, to learn representations for patients and enhance herb interactions. First, we depict the patient portrait to enrich the individualized features. To distinguish personal properties, symptoms, and diagnoses, we adopt the type-aware attention mechanism, thereby improving the accuracy of personalized herb recommendation. Next, we build two herb-interaction graphs and design type-aware multigraph convolution networks to capture the interactions of herbs and patient features. In this way, our model emphasizes the impact of the patient portrait on diagnosis induction and herb selection. Experimental studies demonstrate that our method outperforms the compared methods and confirms the significance of patient portraits. In conclusion, this research proposes type-aware multigraph convolution networks and adds patient portraits modeling to simulate TCM prescriptions making.