GlucoMixer: An Efficient Glucose Monitoring Model with Mixers

With the global prevalence in diabetes and scarcity of definitive clinic schemes, the need for effective and reliable glucose monitoring has become imperative. Due to excellent responsiveness and precision, machine learning (ML) models have been widely employed by continuous glucose monitors (CGMs). However, recent studies have raised concerns that ML-based models often exhibit limited trustworthiness, injecting uncertainty into medical practices. In contrast, deep learning (DL) models are recognized as more trustworthy, but they are struggling with accuracy. To strike a balance between accuracy and trustworthiness, we propose GlucoMixer, an Encoder-only architecture built predominantly with Mixer modules. To prevent future information leakage, we design a Mask Block that employs a lightweight triangular masking scheme. Given the presence of two diabetes types, we employ a convolutional layer to distinguish relevant information and take two Time Mixer Blocks to handle distinct patterns accordingly. Comprehensive In-Distribution (ID) and Out-of-Distribution (OD) evaluations across five benchmark datasets highlight GlucoMixer’s superior performance in high predictive accuracy alongside excellent trustworthiness. According to the test ranking, GlucoMixer is more balanced across multiple evaluation metrics, demonstrating its potential as a practical spur for reliable glucose management and medical decisions.