A Deep-Learning Based Algorithm for the Management of Hyperglycemia in Type 1 Diabetes Therapy

Type 1 diabetes (T1D) is a chronic condition characterized by elevated blood glucose (BG) levels resulting from the pancreas' inability to secrete insulin. To keep their BG levels within the safe range of [70-180] mg/dL, individuals affected by T1D must adhere to a lifelong therapy, which involves multiple daily actions and may negatively impact patients' quality of life. To address these challenges, decision support systems (DSSs) utilizing continuous glucose monitoring sensors have become vital in managing T1D. These tools assist individuals suggesting therapeutic actions like carbohydrate intake and insulin injections. In this context, accurate algorithms predicting future BG levels are essential for proactive interventions, improving glucose control and enhancing patient well-being. To address these challenges, decision support systems (DSSs) utilizing continuous glucose monitoring sensors have become vital in managing T1D. These tools assist individuals suggesting therapeutic actions like carbohydrate intake and insulin injections. In this context, accurate algorithms predicting future BG levels are essential for proactive interventions, improving glucose control and enhancing patient well-being. This paper presents a new algorithm for DSSs that recommends corrective insulin boluses (CIBs). The core of the proposed DSS is a BG predictive algorithm based on a long short-term memory (LSTM) neural network which has been trained on 12 patients with T1D monitored for 10 weeks. The proposed algorithm, named LSTM-CIB, has been retrospectively evaluated on an external test set composed by 30 T1D patients monitored in free-living conditions. Compared to a state-of-art heuristic-based strategy for hyperglycemia correction, LSTM-CIB significantly reduces time spent in hyperglycemia (33.26% vs. 39.42%) and increases time spent in euglycemia (65.29% vs. 59.16%), also improving patient safety.