Investigating Speed Deviation Patterns During Glucose Episodes: A Quantile Regression Approach

• URL: http://arxiv.org/abs/2310.02351v1
• Date: Tue, 3 Oct 2023 18:27:34 GMT
• Title: Investigating Speed Deviation Patterns During Glucose Episodes: A Quantile Regression Approach
• Authors: Aparna Joshi, Jennifer Merickel, Cyrus V. Desouza, Matthew Rizzo, Pujitha Gunaratne, Anuj Sharma
• Abstract summary: Complication of glucose control in diabetes includes hypoglycemic and hyperglycemic episodes, which may impair cognitive and psychomotor functions needed for safe driving. This paper was to determine patterns of diabetes speed behavior during acute glucose to drivers with diabetes who were euglycemic or control drivers without diabetes in a naturalistic driving environment.
• Score: 2.3072218701168166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given the growing prevalence of diabetes, there has been significant interest in determining how diabetes affects instrumental daily functions, like driving. Complication of glucose control in diabetes includes hypoglycemic and hyperglycemic episodes, which may impair cognitive and psychomotor functions needed for safe driving. The goal of this paper was to determine patterns of diabetes speed behavior during acute glucose to drivers with diabetes who were euglycemic or control drivers without diabetes in a naturalistic driving environment. By employing distribution-based analytic methods which capture distribution patterns, our study advances prior literature that has focused on conventional approach of average speed to explore speed deviation patterns.

Related papers

• Chronic Disease Diagnoses Using Behavioral Data [42.96592744768303]
  We aim to diagnose hyperglycemia (diabetes), hyperlipidemia, and hypertension (collectively known as 3H) using own collected behavioral data.
  arXiv  Detail & Related papers  (2024-10-04T12:52:49Z)
• 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)
• Petri nets in modelling glucose regulating processes in the liver [0.0]
  We present a Petri net model of glycolysis and glucose synthesis in the liver. Our analysis shows that the model captures the interactions between different enzymes and substances. The model constitutes the first element of our long-time goal to create the whole body model of the glucose regulation in a healthy human and a person with diabetes.
  arXiv  Detail & Related papers  (2024-05-17T13:15:01Z)
• 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)
• 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)
• Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction [37.846368153741395]
  Task-wise Split Gradient Boosting Trees (TSGB) is proposed for the multi-center diabetes prediction task. TSGB achieves superior performance against several state-of-the-art methods. The proposed TSGB method has been deployed as an online diabetes risk assessment software for early diagnosis.
  arXiv  Detail & Related papers  (2021-08-16T14:22:44Z)
• The Diabetic Buddy: A Diet Regulator andTracking System for Diabetics [3.7026481341955053]
  The prevalence of diabetes in the Middle East is 17-20%, which is well above the global average of 8-9%. This paper presents an automatic way of tracking continuous glucose and food intake of diabetics using off-the-shelf sensors and machine learning.
  arXiv  Detail & Related papers  (2021-01-08T20:03:58Z)
• 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)
• Robustness to Spurious Correlations via Human Annotations [100.63051542531171]
  We present a framework for making models robust to spurious correlations by leveraging humans' common sense knowledge of causality. Specifically, we use human annotation to augment each training example with a potential unmeasured variable. We then introduce a new distributionally robust optimization objective over unmeasured variables (UV-DRO) to control the worst-case loss over possible test-time shifts.
  arXiv  Detail & Related papers  (2020-07-13T20:05:19Z)
• Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles [0.20072624123275526]
  Diabetes is a chronic disease that has a long latent period that complicates detection of the disease at an early stage. We propose to use machine learning to detect the subtle change in the temporal pattern of glucose concentration. Multi-layered perceptrons, convolutional neural networks, and recurrent neural networks all identified the degree of insulin resistance with high accuracy above $85%$.
  arXiv  Detail & Related papers  (2020-05-18T13:31:12Z)
• 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.