Neural Control System for Continuous Glucose Monitoring and Maintenance
- URL: http://arxiv.org/abs/2402.13852v3
- Date: Fri, 7 Jun 2024 11:16:12 GMT
- Title: Neural Control System for Continuous Glucose Monitoring and Maintenance
- Authors: Azmine Toushik Wasi,
- Abstract summary: We provide a novel neural control system for continuous glucose monitoring and management.
Our approach, led by a sophisticated neural policy and differentiable modeling, constantly adjusts insulin supply in real-time.
This end-to-end method maximizes efficiency, providing personalized care and improved health outcomes.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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}.
Related papers
- Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning [4.07484910093752]
In the U.S., over a third of adults are pre-diabetic, with 80% unaware of their status.
Existing wearable glucose monitors are limited by the lack of models trained on small datasets.
arXiv Detail & Related papers (2024-06-12T07:05:53Z) - GluMarker: A Novel Predictive Modeling of Glycemic Control Through Digital Biomarkers [5.311082635540497]
GluMarker is an end-to-end framework for modeling digital biomarkers.
It achieves state-of-the-art on Anderson's dataset in predicting next-day glycemic control.
Research identifies key digital biomarkers for the next day's glycemic control prediction.
arXiv Detail & Related papers (2024-04-19T03:15:50Z) - Toward Short-Term Glucose Prediction Solely Based on CGM Time Series [4.7066018521459725]
TimeGlu is an end-to-end pipeline for short-term glucose prediction based on CGM time series data.
It achieves state-of-the-art performance without the need for additional personal data from patients.
arXiv Detail & Related papers (2024-04-18T06:02:12Z) - Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution [51.83951489847344]
In robotics applications, smooth control signals are commonly preferred to reduce system wear and energy efficiency.
In this work, we aim to bridge this performance gap by growing discrete action spaces from coarse to fine control resolution.
Our work indicates that an adaptive control resolution in combination with value decomposition yields simple critic-only algorithms that yield surprisingly strong performance on continuous control tasks.
arXiv Detail & Related papers (2024-04-05T17:58:37Z) - Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health
Monitoring Systems [69.41229290253605]
Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently.
This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data.
We propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency.
arXiv Detail & Related papers (2024-01-19T16:26:35Z) - Patterns Detection in Glucose Time Series by Domain Transformations and
Deep Learning [0.0]
We describe our research with the aim of predicting the future behavior of blood glucose levels, so that hypoglycemic events may be anticipated.
We have tested our proposed method using real data from 4 different diabetes patients with promising results.
arXiv Detail & Related papers (2023-03-30T09:08:31Z) - Machine Learning based prediction of Glucose Levels in Type 1 Diabetes
Patients with the use of Continuous Glucose Monitoring Data [0.0]
Continuous Glucose Monitoring (CGM) devices offer detailed, non-intrusive and real time insights into a patient's blood glucose concentrations.
Leveraging advanced Machine Learning (ML) Models as methods of prediction of future glucose levels, gives rise to substantial quality of life improvements.
arXiv Detail & Related papers (2023-02-24T19:10:40Z) - Optimal control for state preparation in two-qubit open quantum systems
driven by coherent and incoherent controls via GRAPE approach [77.34726150561087]
We consider a model of two qubits driven by coherent and incoherent time-dependent controls.
The dynamics of the system is governed by a Gorini-Kossakowski-Sudarshan-Lindblad master equation.
We study evolution of the von Neumann entropy, purity, and one-qubit reduced density matrices under optimized controls.
arXiv Detail & Related papers (2022-11-04T15:20:18Z) - On the Robustness of Pretraining and Self-Supervision for a Deep
Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
We compare the impact of different training procedures for diabetic retinopathy grading.
We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions.
Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
arXiv Detail & Related papers (2021-06-25T08:32:45Z) - Adaptive Control and Regret Minimization in Linear Quadratic Gaussian
(LQG) Setting [91.43582419264763]
We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty.
LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
arXiv Detail & Related papers (2020-03-12T19:56:38Z) - Short Term Blood Glucose Prediction based on Continuous Glucose
Monitoring Data [53.01543207478818]
This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools.
We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
arXiv Detail & Related papers (2020-02-06T16:39:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.