Neural Control System for Continuous Glucose Monitoring and Maintenance

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Wasi, Azmine Toushik
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866916279054499840
author Wasi, Azmine Toushik
author_facet Wasi, Azmine Toushik
contents Precise glucose level monitoring is critical for people with diabetes to avoid serious complications. While there are several methods for continuous glucose level monitoring, research on maintenance devices is limited. To mitigate the gap, we provide a novel neural control system for continuous glucose monitoring and management that uses differential predictive control. Our approach, led by a sophisticated neural policy and differentiable modeling, constantly adjusts insulin supply in real-time, thereby improving glucose level optimization in the body. This end-to-end method maximizes efficiency, providing personalized care and improved health outcomes, as confirmed by empirical evidence. Code and data are available at: \url{https://github.com/azminewasi/NeuralCGMM}.
format Preprint
id arxiv_https___arxiv_org_abs_2402_13852
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Neural Control System for Continuous Glucose Monitoring and Maintenance
Wasi, Azmine Toushik
Machine Learning
Artificial Intelligence
Neural and Evolutionary Computing
Systems and Control
Precise glucose level monitoring is critical for people with diabetes to avoid serious complications. While there are several methods for continuous glucose level monitoring, research on maintenance devices is limited. To mitigate the gap, we provide a novel neural control system for continuous glucose monitoring and management that uses differential predictive control. Our approach, led by a sophisticated neural policy and differentiable modeling, constantly adjusts insulin supply in real-time, thereby improving glucose level optimization in the body. This end-to-end method maximizes efficiency, providing personalized care and improved health outcomes, as confirmed by empirical evidence. Code and data are available at: \url{https://github.com/azminewasi/NeuralCGMM}.
title Neural Control System for Continuous Glucose Monitoring and Maintenance
topic Machine Learning
Artificial Intelligence
Neural and Evolutionary Computing
Systems and Control
url https://arxiv.org/abs/2402.13852