Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4086
Title: InsNET: Accurate Basal and Bolus Insulin Dose Prediction for Closed Loop Diabetes Management
Authors: Kalita, Deepjyoti
Mirza, Khalid B.
Keywords: Diabetes management
Deep learning
Insulin dose
Issue Date: Jul-2023
Citation: 45th Annual International Conference of the IEEE Engineering, Medicine and biology society(EMBC 2023), 24-27 July 2023, Austrelia, Sydney
Abstract: It has been demonstrated that closed-loop diabetes management results in better glycemic control and greater compliance than open-loop diabetes management. Deep learning models have been used to implement different components of artifical pancreas. In this work, a novel deep learning model InsNET has been proposed to estimate the basal and bolus insulin level and insulin bolus in patients with type I diabetes utilizing subcutaneous insulin infusion pumps for closed loop diabetes management system. The proposed InsNET is formed with a Wide-Deep combination of LSTM and GRU layers. Additionally, physical activity level has been included as an input in comparison to previous models where only past glucose levels (CGM), meal intake (CHO) and past insulin dosage were used as inputs. The proposed model was tested on In-silico data, and it achieved a Mean Absolute Error (MAE) of 0.002 and Root Mean Squared Error (RMSE) of 0.007 for UVA/Padova Dataset and MAE of 0.001 and RMSE OF 0.003 for mGIPsim Dataset.
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/4086
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2023_EMBC_KMirza_InsNET.pdf4.22 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.