Glucose values prediction five years ahead with a new framework of missing responses in reproducing kernel Hilbert spaces, and the use of continuous glucose monitoring technology

• URL: http://arxiv.org/abs/2012.06564v2
• Date: Mon, 14 Dec 2020 18:47:42 GMT
• Title: Glucose values prediction five years ahead with a new framework of missing responses in reproducing kernel Hilbert spaces, and the use of continuous glucose monitoring technology
• Authors: Marcos Matabuena, Paulo F\'elix, Carlos Meijide-Garcia and Francisco Gude
• Abstract summary: AEGIS study possesses unique information on longitudinal changes in circulating glucose through continuous glucose monitoring technology (CGM) As usual in longitudinal medical studies, there is a significant amount of missing data in the outcome variables. This article proposes a new data analysis framework based on learning in reproducing kernel Hilbert spaces (RKHS) with missing responses.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AEGIS study possesses unique information on longitudinal changes in circulating glucose through continuous glucose monitoring technology (CGM). However, as usual in longitudinal medical studies, there is a significant amount of missing data in the outcome variables. For example, 40 percent of glycosylated hemoglobin (A1C) biomarker data are missing five years ahead. With the purpose to reduce the impact of this issue, this article proposes a new data analysis framework based on learning in reproducing kernel Hilbert spaces (RKHS) with missing responses that allows to capture non-linear relations between variable studies in different supervised modeling tasks. First, we extend the Hilbert-Schmidt dependence measure to test statistical independence in this context introducing a new bootstrap procedure, for which we prove consistency. Next, we adapt or use existing models of variable selection, regression, and conformal inference to obtain new clinical findings about glucose changes five years ahead with the AEGIS data. The most relevant findings are summarized below: i) We identify new factors associated with long-term glucose evolution; ii) We show the clinical sensibility of CGM data to detect changes in glucose metabolism; iii) We can improve clinical interventions based on our algorithms' expected glucose changes according to patients' baseline characteristics.

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)
• 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)
• GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series [12.618792803757714]
  We propose interpretable graph attentive neural networks (GARNNs) to model multi-modal data. GARNNs achieve the best prediction accuracy and provide high-quality temporal interpretability. These findings underline the potential of GARNN as a robust tool for improving diabetes care.
  arXiv  Detail & Related papers  (2024-02-26T01:18:53Z)
• Hypothesis testing for matched pairs with missing data by maximum mean discrepancy: An application to continuous glucose monitoring [0.8399688944263843]
  This paper proposes new estimators of the maximum mean discrepancy (MMD) to handle complex matched pairs with missing data. These estimators can detect differences in data distributions under different missingness mechanisms. Data from continuous glucose monitoring in a longitudinal population-based diabetes study are used to illustrate the application of this approach.
  arXiv  Detail & Related papers  (2022-06-03T14:20:11Z)
• LifeLonger: A Benchmark for Continual Disease Classification [59.13735398630546]
  We introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch. Cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
  arXiv  Detail & Related papers  (2022-04-12T12:25:05Z)
• A k-mer Based Approach for SARS-CoV-2 Variant Identification [55.78588835407174]
  We show that preserving the order of the amino acids helps the underlying classifiers to achieve better performance. We also show the importance of the different amino acids which play a key role in identifying variants and how they coincide with those reported by the USA's Centers for Disease Control and Prevention (CDC)
  arXiv  Detail & Related papers  (2021-08-07T15:08:15Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning [49.86828302591469]
  We train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features.
  arXiv  Detail & Related papers  (2020-04-30T20:42:17Z)
• 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.