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支持自动胰岛素输送系统开发的数学建模研究。

Mathematical modeling research to support the development of automated insulin-delivery systems.

作者信息

Steil Garry M, Reifman Jaques

机构信息

Children's Hospital Boston, Harvard Medical School, Boston, Massachusetts, USA.

出版信息

J Diabetes Sci Technol. 2009 Mar 1;3(2):388-95. doi: 10.1177/193229680900300223.

Abstract

The world leaders in glycemia modeling convened during the Eighth Annual Diabetes Technology Meeting in Bethesda, Maryland, on 14 November 2008, to discuss the current practices in mathematical modeling and make recommendations for its use in developing automated insulin-delivery systems. This report summarizes the collective views of the 25 participating experts in addressing the following four topics: current practices in modeling efforts for closed-loop control; framework for exchange of information and collaboration among research centers; major barriers for the development of accurate models; and key tasks for developing algorithms to build closed-loop control systems. Among the participants, the following main conclusions and recommendations were widely supported: 1. Physiologic variance represents the single largest technical challenge to creating accurate simulation models. 2. A Web site describing different models and the data supporting them should be made publically available, with funding agencies and journals requiring investigators to provide open access to both models and data. 3. Existing simulation models should be compared and contrasted, using the same evaluation and validation criteria, to better assess the state of the art, understand any inherent limitations in the models, and identify gaps in data and/or model capability.

摘要

2008年11月14日,全球血糖建模领域的领军人物齐聚马里兰州贝塞斯达举行的第八届糖尿病技术年度会议,讨论数学建模的当前实践,并就其在开发自动胰岛素输送系统中的应用提出建议。本报告总结了25位与会专家在讨论以下四个主题时的共同观点:闭环控制建模工作的当前实践;研究中心之间信息交流与合作的框架;开发精确模型的主要障碍;以及开发构建闭环控制系统算法的关键任务。与会者中,以下主要结论和建议得到了广泛支持:1. 生理差异是创建精确模拟模型的最大技术挑战。2. 应公开一个描述不同模型及其支持数据的网站,资助机构和期刊要求研究人员提供对模型和数据的开放访问。3. 应使用相同的评估和验证标准对现有模拟模型进行比较和对比,以更好地评估技术现状,了解模型的任何固有局限性,并识别数据和/或模型能力方面的差距。

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