Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction
| Autor: | Edgar N. Sanchez, E. Ruiz-Velázquez, Alma Y. Alanis, Aldo Pardo García, Y. Yuliana Rios, J.A. García-Rodríguez |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Adult
Blood Glucose Uva/Padova simulator Adolescent Neuro-fuzzy Computer science Population Recurrent neural network Machine learning computer.software_genre Fuzzy logic Chart Control theory medicine Diabetes Mellitus Humans Hypoglycemic Agents Insulin Computer Simulation Electrical and Electronic Engineering Child education Instrumentation Type 1 diabetes education.field_of_study Neural multi-step predictor LEMB business.industry Applied Mathematics Type 1 Diabetes Mellitus medicine.disease Computer Science Applications Fuzzy inference Diabetes Mellitus Type 1 Basal (medicine) Control and Systems Engineering Artificial intelligence business computer Algorithms |
| Zdroj: | ISA Transactions-Vol. 126 ISA Transactions Repositorio Institucional UTB Universidad Tecnológica de Bolívar instacron:Universidad Tecnológica de Bolívar |
| Popis: | Diabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific community has directed research in developing technologies to reduce T1DM complications. This contribution is related to a feedback control strategy for blood glucose management in population samples of ten virtual adult subjects, adolescents and children. This scheme focuses on the development of an inverse optimal control (IOC) proposal which is integrated by neural identification, a multi-step prediction (MSP) strategy, and Takagi-Sugeno (T-S) fuzzy inference to shape the convenient insulin infusion in the treatment of T1DM patients. The MSP makes it possible to estimate the glucose dynamics 15 min in advance; therefore, this estimation allows the Neuro-Fuzzy-IOC (NF-IOC) controller to react in advance to prevent hypoglycemic and hyperglycemic events. The T-S fuzzy membership functions are defined in such a way that the respective inferences change basal infusion rates for each patient's condition. The results achieved for scenarios simulated in Uva/Padova virtual software illustrate that this proposal is suitable to maintain blood glucose levels within normoglycemic values (70-115 mg/dL); furthermore, this level remains less than 250 mg/dL during the postprandial event. A comparison between a simple neural IOC (NIOC) and the proposed NF-IOC is carried out using the analysis for control variability named CVGA chart included in the Uva/Padova software. This analysis highlights the improvement of the NF-IOC treatment, proposed in this article, on the NIOC approach because each subject is located inside safe zones for the entire duration of the simulation. |
| Databáze: | OpenAIRE |
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