一种基于李雅普诺夫稳定性和微分博弈理论的机器智能人工胰腺的计算概念验证。
A computational proof of concept of a machine-intelligent artificial pancreas using Lyapunov stability and differential game theory.
作者信息
Greenwood Nigel J C, Gunton Jenny E
机构信息
School of Mathematics and Physics, University of Queensland, Brisbane, Australia Neuromathix, NeuroTech Research Pty Ltd
Westmead Clinical School, University of Sydney, Sydney, Australia Diabetes and Transcription Factors Group, Garvan Institute of Medical Research, Darlinghurst, Australia St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Kensington, Australia Diabetes and Endocrinology, Westmead Hospital, Sydney, Australia.
出版信息
J Diabetes Sci Technol. 2014 Jul;8(4):791-806. doi: 10.1177/1932296814536271. Epub 2014 Jul 4.
BACKGROUND
This study demonstrated the novel application of a "machine-intelligent" mathematical structure, combining differential game theory and Lyapunov-based control theory, to the artificial pancreas to handle dynamic uncertainties.
METHODS
Realistic type 1 diabetes (T1D) models from the literature were combined into a composite system. Using a mixture of "black box" simulations and actual data from diabetic medical histories, realistic sets of diabetic time series were constructed for blood glucose (BG), interstitial fluid glucose, infused insulin, meal estimates, and sometimes plasma insulin assays. The problem of underdetermined parameters was side stepped by applying a variant of a genetic algorithm to partial information, whereby multiple candidate-personalized models were constructed and then rigorously tested using further data. These formed a "dynamic envelope" of trajectories in state space, where each trajectory was generated by a hypothesis on the hidden T1D system dynamics. This dynamic envelope was then culled to a reduced form to cover observed dynamic behavior. A machine-intelligent autonomous algorithm then implemented game theory to construct real-time insulin infusion strategies, based on the flow of these trajectories through state space and their interactions with hypoglycemic or near-hyperglycemic states.
RESULTS
This technique was tested on 2 simulated participants over a total of fifty-five 24-hour days, with no hypoglycemic or hyperglycemic events, despite significant uncertainties from using actual diabetic meal histories with 10-minute warnings. In the main case studies, BG was steered within the desired target set for 99.8% of a 16-hour daily assessment period. Tests confirmed algorithm robustness for ±25% carbohydrate error. For over 99% of the overall 55-day simulation period, either formal controller stability was achieved to the desired target or else the trajectory was within the desired target.
CONCLUSIONS
These results suggest that this is a stable, high-confidence way to generate closed-loop insulin infusion strategies.
背景
本研究展示了一种“机器智能”数学结构的新应用，该结构将微分博弈论与基于李雅普诺夫的控制理论相结合，应用于人工胰腺以应对动态不确定性。
方法
将文献中的真实1型糖尿病（T1D）模型组合成一个复合系统。通过“黑箱”模拟和糖尿病病史的实际数据相结合，构建了血糖（BG）、组织间液葡萄糖、输注胰岛素、膳食估计值以及有时还有血浆胰岛素检测的真实糖尿病时间序列集。通过将遗传算法的一个变体应用于部分信息来规避参数欠定问题，由此构建多个候选个性化模型，然后使用更多数据进行严格测试。这些模型在状态空间中形成了一个“动态包络”轨迹，其中每个轨迹由关于隐藏的T1D系统动力学的一个假设生成。然后将这个动态包络简化以涵盖观察到的动态行为。一种机器智能自主算法随后应用博弈论，根据这些轨迹在状态空间中的流动及其与低血糖或接近高血糖状态的相互作用来构建实时胰岛素输注策略。
结果
该技术在2名模拟参与者身上进行了总共55个24小时日的测试，尽管使用实际糖尿病膳食病史并给出10分钟预警存在显著不确定性，但未发生低血糖或高血糖事件。在主要案例研究中，在每日16小时的评估期内，99.8%的时间里血糖被控制在期望目标范围内。测试证实了算法对于±25%碳水化合物误差的鲁棒性。在整个55天模拟期的99%以上时间里，要么正式控制器达到了期望目标的稳定性，要么轨迹处于期望目标范围内。
结论
这些结果表明，这是一种生成闭环胰岛素输注策略的稳定、高可信度方法。
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