Type 1 diabetes (T1D) management in pediatric patients presents unique challenges due to the evolving physiology and the need for close monitoring and intervention. This article presents the system architecture of TWIN, a novel personalized Decision Support System (DSS) for managing multiple daily injections (MDI) therapy in pediatric T1D patients. TWIN incorporates advanced technologies such as continuous glucose monitoring (CGM) devices, smart insulin pens, and wearable physical activity trackers to collect large amounts of user-generated data. At its core, the system leverages an open-source digital twinning methodology called ReplayBG to create virtual clones of patients, representing their unique physiological characteristics. These virtual clones are then utilized in multiple simulation iterations to personalize and optimize therapy parameters. To promote the transparency of the generated results, TWIN employs a large-scale linguistic model based on a Generative Pre-trained Transformer (GPT). This model provides clear explanations and contextual information regarding the recommended therapy parameters. Finally, the proposed TWIN system architecture integrates these algorithms within a state-of-the-art digital platform. This platform offers a user-friendly interface for patients and healthcare providers, enabling effective management and tuning of MDI therapy. Future work will focus on testing and validating TWIN to assess its efficacy and usability in pediatric T1D management.
System Architecture of TWIN: A New Digital Twin-Based Clinical Decision Support System for Type 1 Diabetes Management in Children
Cappon, G;Pellizzari, E;Cossu, L;Sparacino, G;Facchinetti, A
2023
Abstract
Type 1 diabetes (T1D) management in pediatric patients presents unique challenges due to the evolving physiology and the need for close monitoring and intervention. This article presents the system architecture of TWIN, a novel personalized Decision Support System (DSS) for managing multiple daily injections (MDI) therapy in pediatric T1D patients. TWIN incorporates advanced technologies such as continuous glucose monitoring (CGM) devices, smart insulin pens, and wearable physical activity trackers to collect large amounts of user-generated data. At its core, the system leverages an open-source digital twinning methodology called ReplayBG to create virtual clones of patients, representing their unique physiological characteristics. These virtual clones are then utilized in multiple simulation iterations to personalize and optimize therapy parameters. To promote the transparency of the generated results, TWIN employs a large-scale linguistic model based on a Generative Pre-trained Transformer (GPT). This model provides clear explanations and contextual information regarding the recommended therapy parameters. Finally, the proposed TWIN system architecture integrates these algorithms within a state-of-the-art digital platform. This platform offers a user-friendly interface for patients and healthcare providers, enabling effective management and tuning of MDI therapy. Future work will focus on testing and validating TWIN to assess its efficacy and usability in pediatric T1D management.
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