GluTANN: Transformer-Based Continuous Glucose Monitoring Model with ANN Attention

With dramatically growing tendency of diabetes patients worldwide, effective blood glucose management becomes more and more crucial. Numerous statistical and machine learning models have been developed and equipped with CGM devices to conduct glucose management and supervisions. However, the process of applying such models to clinical practices is quite slow, due to high accuracy but limited certainty. To address such drawback, in this work, we propose an innovative model based on Transformer architecture, GluTANN, with specially designed ANNs acting as self-attentions and paired correlations preserved by the encoder-decoder structure. Extensive experiments across five recognized datasets demonstrate that GluTANN has great competitiveness in reducing uncertainty while preserving satisfying accuracy, providing a feasible approach to effective glucose management and diabetes medical decisions.