使用历史数据的胰岛素输注方案的元启发式优化,并使用患者模拟器进行验证

Hongyu Wang, Lynne M Chepulis, R. Paul, Michael Mayo
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引用次数: 0

摘要

元启发式搜索算法用于开发新的方案,以最佳静脉胰岛素输注率推荐涉及1型糖尿病住院患者的情况下。采用两种元启发式搜索算法,即粒子群优化算法和协方差矩阵自适应进化策略。重症监护患者葡萄糖调节(GRIP)作为优化过程的起点。我们的实验基于文献中的一种方法来评估胰岛素方案的有利性,使用从怀卡托区卫生委员会获得的16名患者的血糖水平/胰岛素输注率时间序列记录数据集。通过元启发式搜索从数据中发现新的和明显优于GRIP的胰岛素输注策略。新发现的策略被进一步验证,并在使用虚拟患者模拟器的各种竞争性基准测试中显示出良好的性能。
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Metaheuristic Optimization of Insulin Infusion Protocols Using Historical Data with Validation Using a Patient Simulator
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.
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