Development of a neural network model architecture for personalized insulin dosage calculation in type I diabetes

Type I diabetes is a chronic autoimmune disease characterized by insulin hormone deficiency in the body, requiring patients to rely on external administration of this hormone for survival. Additionally, blood glucose monitoring and precise insulin dose calculation are essential aspects in managing this disease. The current research proposes an approach that uses machine learning techniques to optimize and personalize insulin dose calculations for patients with type I diabetes. The developed model is based on neural networks and has been trained using pharmacodynamic profiles and a simulated dataset. The research was conducted over a period of two months, focusing on adapting the algorithm to individual patient characteristics to improve blood glucose level management.