Toward Short-Term Glucose Prediction Solely Based on CGM Time Series

• URL: http://arxiv.org/abs/2404.11924v1
• Date: Thu, 18 Apr 2024 06:02:12 GMT
• Title: Toward Short-Term Glucose Prediction Solely Based on CGM Time Series
• Authors: Ming Cheng, Xingjian Diao, Ziyi Zhou, Yanjun Cui, Wenjun Liu, Shitong Cheng,
• Abstract summary: 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.
• Score: 4.7066018521459725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The global diabetes epidemic highlights the importance of maintaining good glycemic control. Glucose prediction is a fundamental aspect of diabetes management, facilitating real-time decision-making. Recent research has introduced models focusing on long-term glucose trend prediction, which are unsuitable for real-time decision-making and result in delayed responses. Conversely, models designed to respond to immediate glucose level changes cannot analyze glucose variability comprehensively. Moreover, contemporary research generally integrates various physiological parameters (e.g. insulin doses, food intake, etc.), which inevitably raises data privacy concerns. To bridge such a research gap, we propose TimeGlu -- an end-to-end pipeline for short-term glucose prediction solely based on CGM time series data. We implement four baseline methods to conduct a comprehensive comparative analysis of the model's performance. Through extensive experiments on two contrasting datasets (CGM Glucose and Colas dataset), TimeGlu achieves state-of-the-art performance without the need for additional personal data from patients, providing effective guidance for real-world diabetic glucose management.

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)
• FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions [4.073768455373616]
  Continuous glucose monitoring (CGM) devices provide real-time glucose monitoring and timely alerts for glycemic excursions. Rare events like hypoglycemia and hyperglycemia remain challenging due to their infrequency. We propose a novel Hypo-Hyper (HH) loss function, which significantly improves performance in the glycemic excursion regions.
  arXiv  Detail & Related papers  (2024-08-25T19:51:27Z)
• 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)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• CrossGP: Cross-Day Glucose Prediction Excluding Physiological Information [4.975538965305628]
  Early glucose prediction for diabetic patients is necessary for timely medical treatment. We propose CrossGP, a novel machine-learning framework for cross-day glucose prediction.
  arXiv  Detail & Related papers  (2024-04-16T20:40:59Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• 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)
• Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks [5.250950284616893]
  We study the problem of blood glucose forecasting and provide a deep personalized solution. We analyze the data and detect important patterns. We empirically show the efficacy of our model on a real dataset.
  arXiv  Detail & Related papers  (2021-06-02T01:36:53Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)
• Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data [75.23250968928578]
  Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
  arXiv  Detail & Related papers  (2020-09-25T01:50:15Z)
• 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.