GluMarker: A Novel Predictive Modeling of Glycemic Control Through Digital Biomarkers

• URL: http://arxiv.org/abs/2404.12605v1
• Date: Fri, 19 Apr 2024 03:15:50 GMT
• Title: GluMarker: A Novel Predictive Modeling of Glycemic Control Through Digital Biomarkers
• Authors: Ziyi Zhou, Ming Cheng, Xingjian Diao, Yanjun Cui, Xiangling Li,
• Abstract summary: 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.
• Score: 5.311082635540497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The escalating prevalence of diabetes globally underscores the need for diabetes management. Recent research highlights the growing focus on digital biomarkers in diabetes management, with innovations in computational frameworks and noninvasive monitoring techniques using personalized glucose metrics. However, they predominantly focus on insulin dosing and specific glucose values, or with limited attention given to overall glycemic control. This leaves a gap in expanding the scope of digital biomarkers for overall glycemic control in diabetes management. To address such a research gap, we propose GluMarker -- an end-to-end framework for modeling digital biomarkers using broader factors sources to predict glycemic control. Through the assessment and refinement of various machine learning baselines, GluMarker achieves state-of-the-art on Anderson's dataset in predicting next-day glycemic control. Moreover, our research identifies key digital biomarkers for the next day's glycemic control prediction. These identified biomarkers are instrumental in illuminating the daily factors that influence glycemic management, offering vital insights for diabetes care.

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)
• Exploring Biomarker Relationships in Both Type 1 and Type 2 Diabetes Mellitus Through a Bayesian Network Analysis Approach [1.004996690798013]
  This study applies Bayesian network structure learning to analyze the Shanghai Type 1 and Type 2 diabetes mellitus datasets. The constructed Bayesian network presented notable predictive accuracy, particularly for Type 2 diabetes mellitus, with root mean squared error (RMSE) of 18.23 mg/dL.
  arXiv  Detail & Related papers  (2024-06-24T19:27:34Z)
• MMIL: A novel algorithm for disease associated cell type discovery [58.044870442206914]
  Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. We introduce Mixture Modeling for Multiple Learning Instance (MMIL), an expectation method that enables the training and calibration of cell-level classifiers.
  arXiv  Detail & Related papers  (2024-06-12T15:22:56Z)
• 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)
• Neural Control System for Continuous Glucose Monitoring and Maintenance [0.0]
  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.
  arXiv  Detail & Related papers  (2024-02-21T14:56:36Z)
• Tertiary Lymphoid Structures Generation through Graph-based Diffusion [54.37503714313661]
  In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs. We show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content.
  arXiv  Detail & Related papers  (2023-10-10T14:37:17Z)
• 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)
• Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction [102.23901690661916]
  Low-dimensional embedding spaces can be derived from the UK Biobank population dataset to enhance data-scarce prediction of health indicators, lifestyle and demographic characteristics. Performances gains from semisupervison approaches will probably become an important ingredient for various medical data science applications.
  arXiv  Detail & Related papers  (2021-10-12T16:25:50Z)
• Cancer Gene Profiling through Unsupervised Discovery [49.28556294619424]
  We introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. Our signature reports promising results on distinguishing immune inflammatory and immune desert tumors.
  arXiv  Detail & Related papers  (2021-02-11T09:04:45Z)
• 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.