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Abstract Nocturnal hypoglycemia is a common phenomenon among patients with diabetes and can lead to a broad range of adverse events and complications. Identifying factors associated with hypoglycemia can improve glucose control and patient care. We propose a repeated measures random forest (RMRF) algorithm that can handle nonlinear relationships and interactions and the correlated responses from patients evaluated over several nights. Simulation results show that our proposed algorithm captures the informative variable more often than naïvely assuming independence. RMRF also outperforms standard random forest and extremely randomized trees algorithms. We demonstrate scenarios where RMRF attains greater prediction accuracy than generalized linear models. We apply the RMRF algorithm to analyze a diabetes study with 2524 nights from 127 patients with type 1 diabetes. We find that nocturnal hypoglycemia is associated with HbA1c, bedtime blood glucose (BG), insulin on board, time system activated, exercise intensity, and daytime hypoglycemia. The RMRF can accurately classify nights at high risk of nocturnal hypoglycemia.
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Design and Validation of an Open-Source Closed-Loop Testbed for Artificial Pancreas Systems
The development of fully autonomous artificial pancreas systems (APS) that independently regulate the glucose levels of patients with Type 1 diabetes has been a long-standing goal of diabetes research. A significant barrier to progress is the difficulty of testing new control algorithms and safety features, since clinical trials are time- and resource-intensive. To facilitate ease of validation, we propose an open-source APS testbed that can integrate state-of-the-art APS controllers and glucose simulators with a novel fault injection engine. The testbed is used to reproduce the blood glucose trajectories of real patients from a clinical trial conducted over six months. We evaluate the performance of two closed-loop control algorithms (OpenAPS and Basal Bolus) using the testbed and find that these control algorithms are able to keep blood glucose in a safe region 93.49% and 79.46% of the time on average, compared with 66.18% of the time for the clinical trial. The fault injection engine simulates the real recalls and adverse events reported to the U.S. Food and Drug Administration (FDA) and demonstrates the resilience of the controller in hazardous conditions. We use the testbed to generate 2.5 years of synthetic data representing 20 different patient profiles with realistic adverse event scenarios, which would have been expensive and risky to collect in a clinical trial.
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- PAR ID:
- 10411985
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2022 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
- ISSN:
- 2832-2975
- ISBN:
- 978-1-4503-9476-5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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