<![CDATA[Maintaining blood glucose (BG) levels within the euglycemic range is essential for patients with type 1 diabetes (T1D) to prevent both hypoglycemia and hyperglycemia. Often, BG concentration changes due to unannounced carbohydrate intake during meals or an inappropriate amount of insulin dosage. Timely forecasting of BG can help take appropriate actions in advance to keep BG within the euglycemic range. Recent studies indicate that deep learning techniques have demonstrated improved performance in this field. Deep learning approaches often struggle to precisely predict future BG levels. To address these challenges, this paper introduces a novel hybrid deep learning architecture called DCRNet. This architecture incorporates a Dilated Convolution layer that effectively detects multi-scale patterns while minimizing parameter count. Additionally, it utilizes Long Short-Term Memory (LSTM) to handle contextual dependencies and maintain the temporal order of the extracted features. DCRNet predicts future BG levels for short-term durations (15, 30, and 60 minutes) using information on glucose, meals, and insulin dosages. The proposed architecture’s performance is evaluated on 11 simulated subjects from the UVA/Padova T1D Mellitus simulator and 12 actual subjects from the OhioT1DM dataset. In contrast to previous works, the proposed architecture achieves root mean square errors (RMSEs) of 3.42, 6.45, and 17.73 mg/dL for simulated subjects and 12.57, 20.72, and 34.41mg/dL for actual subjects, for prediction horizons (PH) of 15-, 30-, and 60-minute, respectively. The proposed architecture is also evaluated using the mean absolute error (MAE), which is 2.11, 4.47, and 11.78 mg/dL for simulated subjects and 7.9, 14.13, and 25.5 mg/dL for actual subjects, for 15-, 30-, and 60-minute PH. The experimental findings validate that the proposed architecture, which uses a dilated convolutional LSTM, outperforms other recent state-of-the-art models.]]>
