The management of blood glucose levels is critical in the care of Type 1 diabetes subjects. In extremes, high or low levels of blood glucose are fatal. To avoid such adverse events, there is the development and adoption of wearable technologies that continuously monitor blood glucose and administer insulin. This technology allows subjects to easily track their blood glucose levels with early intervention without the need for hospital visits. The data collected
from these sensors is an excellent candidate for the application of machine learning algorithms to learn patterns and predict future values of blood glucose levels. In this study, we developed artificial neural network algorithms based on the OhioT1DM training dataset that
contains data on 12 subjects. The dataset contains features such as subject identifiers, continuous glucose monitoring data obtained in 5 minutes intervals, insulin infusion rate, etc. We developed individual models, including LSTM, BiLSTM, Convolutional LSTMs, TCN, and sequence-to-sequence models. We also developed transfer learning models based on the most important features of the data, as identified by a gradient boosting algorithm. These models were evaluated on the OhioT1DM test dataset that contains 6 unique subject’s data. The model with the lowest RMSE values for the 30- and 60-minutes was selected as the best performing model. Our result shows that sequence-to-sequence BiLSTM performed better than the other models. This work demonstrates the potential of artificial neural networks
algorithms in the management of Type 1 diabetes.
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Wearable Glucose Monitoring and Implantable Drug Delivery Systems for Diabetes Management
The global cost of diabetes care exceeds $1 trillion each year with more than $327 billion being spent in the United States alone. Despite some of the advances in diabetes care including continuous glucose monitoring systems and insulin pumps, the technology associated with managing diabetes has largely remained unchanged over the past several decades. With the rise of wearable electronics and novel functional materials, the field is well‐poised for the next generation of closed‐loop diabetes care. Wearable glucose sensors implanted within diverse platforms including skin or on‐tooth tattoos, skin‐mounted patches, eyeglasses, contact lenses, fabrics, mouthguards, and pacifiers have enabled noninvasive, unobtrusive, and real‐time analysis of glucose excursions in ambulatory care settings. These wearable glucose sensors can be integrated with implantable drug delivery systems, including an insulin pump, glucose responsive insulin release implant, and islets transplantation, to form self‐regulating closed‐loop systems. This review article encompasses the emerging trends and latest innovations of wearable glucose monitoring and implantable insulin delivery technologies for diabetes management with a focus on their advanced materials and construction. Perspectives on the current unmet challenges of these strategies are also discussed to motivate future technological development toward improved patient care in diabetes management.
more » « less- Award ID(s):
- 1944480
- NSF-PAR ID:
- 10449054
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Healthcare Materials
- Volume:
- 10
- Issue:
- 17
- ISSN:
- 2192-2640
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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