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

• GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks [0.12564343689544843]
  Continuous glucose monitors (CGM) are small medical devices that measure blood glucose levels at regular intervals. Forecasting of glucose trajectories based on CGM data holds the potential to substantially improve diabetes management.
  arXiv  Detail & Related papers  (2024-10-08T08:01:09Z)
• From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [50.80532910808962]
  We present GluFormer, a generative foundation model on biomedical temporal data based on a transformer architecture. GluFormer generalizes to 15 different external datasets, including 4936 individuals across 5 different geographical regions. It can also predict onset of future health outcomes even 4 years in advance.
  arXiv  Detail & Related papers  (2024-08-20T13:19:06Z)
• Hearing Your Blood Sugar: Non-Invasive Glucose Measurement Through Simple Vocal Signals, Transforming any Speech into a Sensor with Machine Learning [0.0]
  We present a transformative and straightforward method that utilizes voice analysis to predict blood glucose levels. By applying advanced machine learning algorithms, we analyzed vocal signal variations and established a significant correlation with blood glucose levels. Our findings indicate that voice analysis may serve as a viable non-invasive alternative for glucose monitoring.
  arXiv  Detail & Related papers  (2024-08-15T12:13:23Z)
• 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)
• 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)
• 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)
• 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.