Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model with Modified Error Rate

• URL: http://arxiv.org/abs/2502.14183v1
• Date: Thu, 20 Feb 2025 01:26:00 GMT
• Title: Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model with Modified Error Rate
• Authors: Saman Khamesian, Asiful Arefeen, Adela Grando, Bithika Thompson, Hassan Ghasemzadeh,
• Abstract summary: We develop GLIMMER, a machine learning approach for forecasting blood glucose levels. GLIMMER categorizes glucose values into normal and abnormal ranges and devises a novel custom loss function to prioritize accuracy in dysglycemic events.
• Score: 6.300322064585917
• License:
• Abstract: Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels to avert the dangers of dysglycemia (hyperglycemia or hypoglycemia). Despite the advent of sophisticated technologies such as automated insulin delivery (AID) systems, achieving optimal glycemic control remains a formidable task. AID systems integrate continuous subcutaneous insulin infusion (CSII) and continuous glucose monitors (CGM) data, offering promise in reducing variability and increasing glucose time-in-range. However, these systems often fail to prevent dysglycemia, partly due to limitations in prediction algorithms that lack the precision to avert abnormal glucose events. This gap highlights the need for proactive behavioral adjustments. We address this need with GLIMMER, Glucose Level Indicator Model with Modified Error Rate, a machine learning approach for forecasting blood glucose levels. GLIMMER categorizes glucose values into normal and abnormal ranges and devises a novel custom loss function to prioritize accuracy in dysglycemic events where patient safety is critical. To evaluate the potential of GLIMMER for T1D management, we both use a publicly available dataset and collect new data involving 25 patients with T1D. In predicting next-hour glucose values, GLIMMER achieved a root mean square error (RMSE) of 23.97 (+/-3.77) and a mean absolute error (MAE) of 15.83 (+/-2.09) mg/dL. These results reflect a 23% improvement in RMSE and a 31% improvement in MAE compared to the best-reported error rates.

Related papers

• Let Curves Speak: A Continuous Glucose Monitor based Large Sensor Foundation Model for Diabetes Management [3.8195320624847833]
  Integrating AI with continuous glucose monitoring holds promise for near-future glucose prediction. CGM-LSM is pretrained on 15.96 million glucose records from 592 diabetes patients for near-future glucose prediction. LSM achieved exceptional performance, with an rMSE of 29.81 mg/dL for type 1 diabetes patients and 23.49 mg/dL for type 2 diabetes patients in a two-hour prediction horizon.
  arXiv  Detail & Related papers  (2024-12-12T21:35:13Z)
• Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Data [45.75724873443564]
  Domain shift poses a significant challenge in clinical applications, e.g., Diabetic Retinopathy grading. We propose a novel approach, Generative Unadversarial ExampleS (GUES), which enables adaptation from a data-centric perspective.
  arXiv  Detail & Related papers  (2024-12-02T07:14:25Z)
• Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting [6.466206145151128]
  GlucoNet is an AI-powered sensor system for continuously monitoring behavioral and physiological health. We propose a decomposition-based transformer model that incorporates patients' behavioral and physiological data. G GlucoNet achieves a 60% improvement in RMSE and a 21% reduction in the number of parameters, using data obtained involving 12 participants with T1-Diabetes.
  arXiv  Detail & Related papers  (2024-11-16T05:09:20Z)
• 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 [47.23780364438969]
  We present GluFormer, a generative foundation model for CGM data that learns nuanced glycemic patterns and translates them into predictive representations of metabolic health. GluFormer generalizes to 19 external cohorts spanning different ethnicities and ages, 5 countries, 8 CGM devices, and diverse pathophysiological states. In a longitudinal study of 580 adults with CGM data and 12-year follow-up, GluFormer identifies individuals at elevated risk of developing diabetes more effectively than blood HbA1C%.
  arXiv  Detail & Related papers  (2024-08-20T13:19:06Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• 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)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis [50.231954872304314]
  We propose an Adaptive Curriculum Learning framework, which adaptively discovers and discards the samples with inconsistent labels. We also contribute TNCD: a Thyroid Nodule Classification dataset.
  arXiv  Detail & Related papers  (2022-07-02T11:50:02Z)
• Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction [4.040272012640556]
  We propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model. For the OhioT1DM dataset, containing eight weeks' data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 minutes and 60 minutes of prediction horizon (PH) Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management.
  arXiv  Detail & Related papers  (2021-01-18T02:31:38Z)
• 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.