Blood Glucose Level Prediction: A Graph-based Explainable Method with
  Federated Learning
Chengzhe Piao Ken Li
In the UK, approximately 400,000 people with type 1 diabetes (T1D) rely on
insulin delivery due to insufficient pancreatic insulin production. Managing
blood glucose (BG) levels is crucial, with continuous glucose monitoring (CGM)
playing a key role. CGM, tracking BG every 5 minutes, enables effective blood
glucose level prediction (BGLP) by considering factors like carbohydrate intake
and insulin delivery.
  Recent research has focused on developing sequential models for BGLP using
historical BG data, incorporating additional attributes such as carbohydrate
intake, insulin delivery, and time. These methods have shown notable success in
BGLP, with some providing temporal explanations. However, they often lack clear
correlations between attributes and their impact on BGLP. Additionally, some
methods raise privacy concerns by aggregating participant data to learn
population patterns.
  Addressing these limitations, we introduced a graph attentive memory (GAM)
model, combining a graph attention network (GAT) with a gated recurrent unit
(GRU). GAT applies graph attention to model attribute correlations, offering
transparent, dynamic attribute relationships. Attention weights dynamically
gauge attribute significance over time. To ensure privacy, we employed
federated learning (FL), facilitating secure population pattern analysis.
  Our method was validated using the OhioT1DM'18 and OhioT1DM'20 datasets from
12 participants, focusing on 6 key attributes. We demonstrated our model's
stability and effectiveness through hyperparameter impact analysis.