Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4086
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dc.contributor.authorKalita, Deepjyoti-
dc.contributor.authorMirza, Khalid B.-
dc.date.accessioned2023-11-14T05:09:46Z-
dc.date.available2023-11-14T05:09:46Z-
dc.date.issued2023-07-
dc.identifier.citation45th Annual International Conference of the IEEE Engineering, Medicine and biology society(EMBC 2023), 24-27 July 2023, Austrelia, Sydneyen_US
dc.identifier.urihttp://hdl.handle.net/2080/4086-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractIt 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.en_US
dc.language.isoenen_US
dc.subjectDiabetes managementen_US
dc.subjectDeep learningen_US
dc.subjectInsulin doseen_US
dc.titleInsNET: Accurate Basal and Bolus Insulin Dose Prediction for Closed Loop Diabetes Managementen_US
dc.typePresentationen_US
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