11 research outputs found
Estimating a Personalized Basal Insulin Dose from Short-Term Closed-Loop Data in Type 2 Diabetes
In type 2 diabetes (T2D) treatment, finding a safe and effective basal
insulin dose is a challenge. The dose-response is highly individual and to
ensure safety, people with T2D titrate by slowly increasing the daily insulin
dose to meet treatment targets. This titration can take months. To ease and
accelerate the process, we use short-term artificial pancreas (AP) treatment
tailored for initial titration and apply it as a diagnostic tool. Specifically,
we present a method to automatically estimate a personalized daily dose of
basal insulin from closed-loop data collected with an AP. Based on AP-data from
a stochastic simulation model, we employ the continuous-discrete extended
Kalman filter and a maximum likelihood approach to estimate parameters in a
simple dose-response model for 100 virtual people. With the identified model,
we compute a daily dose of basal insulin to meet treatment targets for each
individual. We test the personalized dose and evaluate the treatment outcomes
against clinical reference values. In the tested simulation setup, the proposed
method is feasible. However, more extensive tests will reveal whether it can be
deemed safe for clinical implementation.Comment: 6 pages, 4 figures, 1 table. Accepted for publication in Proceedings
of the 2022 61st IEEE Conference on Decision and Control (CDC