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Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...
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Long-Term Glucose Forecasting for Open-Source Automated Insulin Delivery Systems: A Machine Learning Study with

Ahtsham Zafar1, Dana M Lewis2, Arsalan Shahid3

  • 1School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan.

Healthcare (Basel, Switzerland)
|March 29, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning and deep learning accurately forecast glucose variability up to 48 hours in advance for individuals using automated insulin delivery systems. These models offer reliable glucose forecasting and variability analysis for diabetes management.

Keywords:
AIDOpenAPSautomated insulin deliveryclosed loopglucose forecastingglucose variabilityglycemic variabilitylarge-scale diabetes dataset

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Diabetes Technology

Background:

  • Glucose forecasting is crucial for diabetes management, particularly for real-time insulin dosing and optimizing physical activity.
  • Open-source automated insulin delivery (AID) systems generate large datasets valuable for developing predictive models.

Purpose of the Study:

  • To evaluate machine learning (ML) and deep learning (DL) methods for predicting glucose variability (GV) in individuals using open-source AID systems.
  • To assess the accuracy, performance, and resource costs of various ML/DL models for glucose forecasting up to 48 hours ahead.

Main Methods:

  • A three-stage framework: data collection, data preparation/analysis, and ML/DL model development/evaluation.
  • Systematic implementation and fine-tuning of ML/DL models on a large-scale diabetes dataset.
  • Evaluation of 17 GV metrics, including mean absolute error (MAE) and root mean square error (RMSE), alongside model execution time and memory consumption.

Main Results:

  • Fine-tuned ML/DL models demonstrated considerable accuracy in glucose forecasting and variability analysis up to 48 hours in advance.
  • Long Short-Term Memory (LSTM) models achieved the lowest average MAE (2.50 mg/dL) and RMSE (3.7 mg/dL), outperforming Autoregressive Integrated Moving Average (ARIMA) models.
  • Model execution time correlated with training data size; LSTM models had the lowest execution time but highest memory usage.

Conclusions:

  • ML/DL methods, particularly LSTM, show significant potential for accurate glucose forecasting and variability analysis in AID systems.
  • The study highlights the importance of considering model performance, resource costs, and sustainable implementation for real-world applications.
  • This research supports the integration of advanced computational tools for enhanced diabetes management through AID systems.