TY - JOUR
TI - Mongolian medicine prescription recommendation using graph attention networks leveraging semantic associations for precise predictions
AU - Han Shuqin
AU - Bao Sandan
AU - Li Haibin
JN - Thermal Science
PY - 2026
VL - 30
IS - 2
SP - 1107
EP - 1116
PT - Article
AB - The objective of this study is to address the challenges faced by traditional  Mongolian medicine in the modern era. The complex knowledge system and  challenges related to inheritance in Mongolian medicine represent  significant obstacles to the modern development of this discipline. The  present study introduces a graph attention network (GAT) model to address  these issues. The GAT model establishes graphs of symptoms, Mongolian  medicine, and symptoms-Mongolian medicine. The GAT model employs graph  convolution operations to effectively capture the intricate relationships  among symptoms and Mongolian medicines. This facilitates the model capacity  to discern representations that are both discriminative of symptoms and  Mongolian medicines. Consequently, the model is capable of matching  appropriate Mongolian medicinal prescriptions according to the input  symptoms. A series of experimental evaluations were conducted on a dataset  derived from the Encyclopedia of Mongolian Medicine. These evaluations  demonstrated that the proposed GAT model outperforms existing models in  terms of prescription recommendation accuracy. Specifically, the model  achieves an accuracy of 37.59%, representing significant improvements  compared to other models. These findings suggest that the GAT model can  effectively leverage the relationships among symptoms and Mongolian  medicines to provide reliable prescription recommendations, offering a  promising solution for the modernization of Mongolian medicine.
DO - 10.2298/TSCI2602107H
ER -