Local-Global Context Encoding Architecture for the Insulin Prescription Recommendation

Blood glucose levels, when analyzed as time series data, contain vital information about a patient's glucose fluctuations and trends, which are crucial for assessing pancreatic function and insulin sensitivity. Leveraging these insights through feature extraction and classification can help determine the optimal insulin dosage for patients. Traditional methods of manual feature selection and extraction are laborintensive and inefficient. In this paper, we propose a novel local-global context encoding architecture to extract deep features from blood glucose time series data. We employ a weighted k-nearest neighbors (KNN) classifier to provide dynamic and personalized treatment recommendations. Our experiments, conducted using the UVA/Padova type 1 diabetes simulator and the ShanghaiT2DM real-world diabetes dataset, demonstrate that deep features significantly outperform manually extracted features in recommending insulin dosages. Our approach offers a more accurate and personalized method for managing multiple daily insulin injections, potentially reducing the workload of medical practitioners and improving the efficiency and effectiveness of diabetes management. Code is available at https://github.com/EricWvi/ykw.