التفاصيل البيبلوغرافية
| العنوان: |
Long-Term Glucose Forecasting for Open-Source Automated Insulin Delivery Systems: A Machine Learning Study with Real-World Variability Analysis. |
| المؤلفون: |
Zafar, Ahtsham, Lewis, Dana M., Shahid, Arsalan |
| المصدر: |
Healthcare (2227-9032); Mar2023, Vol. 11 Issue 6, p779, 21p |
| مصطلحات موضوعية: |
BLOOD sugar analysis, DEEP learning, MACHINE learning, AUTOMATION, INSULIN pumps, RESEARCH funding, DESCRIPTIVE statistics, DATA analysis software, ALGORITHMS |
| مستخلص: |
Glucose forecasting serves as a backbone for several healthcare applications, including real-time insulin dosing in people with diabetes and physical activity optimization. This paper presents a study on the use of machine learning (ML) and deep learning (DL) methods for predicting glucose variability (GV) in individuals with open-source automated insulin delivery systems (AID). A three-stage experimental framework is employed in this work to systematically implement and evaluate ML/DL methods on a large-scale diabetes dataset collected from individuals with open-source AID. The first stage involves data collection, the second stage involves data preparation and exploratory analysis, and the third stage involves developing, fine-tuning, and evaluating ML/DL models. The performance and resource costs of the models are evaluated alongside relative and proportional errors for 17 GV metrics. Evaluation of fine-tuned ML/DL models shows considerable accuracy in glucose forecasting and variability analysis up to 48 h in advance. The average MAE ranges from 2.50 mg/dL for long short-term memory models (LSTM) to 4.94 mg/dL for autoregressive integrated moving average (ARIMA) models, and the RMSE ranges from 3.7 mg/dL for LSTM to 7.67 mg/dL for ARIMA. Model execution time is proportional to the amount of data used for training, with long short-term memory models having the lowest execution time but the highest memory consumption compared to other models. This work successfully incorporates the use of appropriate programming frameworks, concurrency-enhancing tools, and resource and storage cost estimators to encourage the sustainable use of ML/DL in real-world AID systems. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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