Metaheuristic optimization of insulin infusion protocols using historical data with validation using a patient simulator
Wang, H., Chepulis, L. M., Paul, R. G., & Mayo, M. (2021). Metaheuristic optimization of insulin infusion protocols using historical data with validation using a patient simulator. Vietnam Journal of Computer Science, 8(2), 263–290. https://doi.org/10.1142/s2196888821500111
Permanent Research Commons link: https://hdl.handle.net/10289/14148
Metaheuristic search algorithms are used to develop new protocols for optimal intravenous insulin infusion rate recommendations in scenarios involving hospital in-patients with Type 1 Diabetes. Two metaheuristic search algorithms are used, namely, Particle Swarm Optimization and Covariance Matrix Adaption Evolution Strategy. The Glucose Regulation for Intensive Care Patients (GRIP) serves as the starting point of the optimization process. We base our experiments on a methodology in the literature to evaluate the favorability of insulin protocols, with a dataset of blood glucose level/insulin infusion rate time series records from 16 patients obtained from the Waikato District Health Board. New and significantly better insulin infusion strategies than GRIP are discovered from the data through metaheuristic search. The newly discovered strategies are further validated and show good performance against various competitive benchmarks using a virtual patient simulator.
© The authors. This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.