Graph Encoding-Enhanced Transformer for Drug Recommendation
Xunsheng Cai, Syauki Aulia Thamrin, Arbee L. P. Chen · 2023
Doctors prescribe drugs for the patient with the objective of curing the patient. Some drugs cannot be consumed together since doing so may cause negative effects. This can be avoided by knowing the effects caused by consuming combinations of drugs. However, for complex cases of a patient, it can be difficult to decide the best combination of drugs. Therefore, automatic drug recommendation method was used to recommend drugs with minimal negative effects. It is performed by using a deep learning model which is trained on drug data. A graph called drug-drug interaction (DDI) is used to represent the drugs and effects of consuming one drug with other drugs. Additionally, information about the combination of drugs prescribed in the past for a patient is also important for drug recommendation. It can also be represented as a graph called drug concurrence relation (DCR). The DDI and DCR graphs can be input to the deep learning model through an encoding process. In this paper, we propose a graph encoding-enhanced transformer (GEET) to recommend drugs. The DDI and DCR graphs are encoded by using Graph Attention Network (GAT). The graph encoding model has multi-head attention, which makes the GEET model aware of the most important DDI and DCR from the graphs. Additionally, the encoding outputs are combined, and activation function and normalization methods are used to improve the performance. The model has been evaluated on the publicly available MIMIC-III dataset and has the best results on F1, Jaccard and PRAUC scores compared to the models proposed by the existing related research papers.