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人工胰腺算法的开发平台。

Development platform for artificial pancreas algorithms.

机构信息

Computer Science Department, Faculty of Science, Kuwait University, Kuwait City, Kuwait.

Department of Nutrition, Faculty of Medicine, Université de Montréal, Montréal, Canada.

出版信息

PLoS One. 2020 Dec 17;15(12):e0243139. doi: 10.1371/journal.pone.0243139. eCollection 2020.

DOI:10.1371/journal.pone.0243139
PMID:33332411
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7746189/
Abstract

BACKGROUND AND AIMS

Assessing algorithms of artificial pancreas systems is critical in developing automated and fault-tolerant solutions that work outside clinical settings. The development and evaluation of algorithms can be facilitated with a platform that conducts virtual clinical trials. We present in this paper a clinically validated cloud-based distributed platform that supports the development and comprehensive testing of single and dual-hormone algorithms for type 1 diabetes mellitus (T1DM).

METHODS

The platform is built on principles of object-oriented design and runs user algorithms in real-time virtual clinical trials utilizing a multi-threaded environment enabled by concurrent execution over a cloud infrastructure. The platform architecture isolates user algorithms located on personal machines from proprietary patient data running on the cloud. Users import a plugin into their algorithms (Matlab, Python, or Java) to connect to the platform. Once connected, users interact with a graphical interface to design experimental protocols for their trials. Protocols include trial duration in days, mealtimes and amounts, variability in mealtimes and amounts, carbohydrate counting errors, snacks, and onboard insulin levels.

RESULTS

The platform facilitates development by solving the ODE model in the cloud on large CPU-optimized machines, providing a 62% improvement in memory, speed and CPU utilization. Users can easily debug & modify code, test multiple strategies, and generate detailed clinical performance reports. We validated and integrated into the platform a glucoregulatory system of ordinary differential equations (ODEs) parameterized with clinical data to mimic the inter and intra-day variability of glucose responses of 15 T1DM patients.

CONCLUSION

The platform utilizes the validated patient model to conduct virtual clinical trials for the rapid development and testing of closed-loop algorithms for T1DM.

摘要

背景与目的

评估人工胰腺系统的算法对于开发在临床环境之外工作的自动化和容错解决方案至关重要。可以使用进行虚拟临床试验的平台来促进算法的开发和评估。我们在此介绍一个经过临床验证的基于云的分布式平台，该平台支持开发和全面测试用于 1 型糖尿病 (T1DM) 的单激素和双激素算法。

方法

该平台基于面向对象设计的原则，利用云基础架构上的并行执行实现的多线程环境，在实时虚拟临床试验中运行用户算法。该平台架构将位于个人计算机上的用户算法与在云中运行的专有患者数据隔离开来。用户将插件导入到他们的算法（Matlab、Python 或 Java）中以连接到平台。连接后，用户可以通过图形界面与平台交互，为他们的试验设计实验方案。方案包括试验持续时间（以天为单位）、进餐时间和量、进餐时间和量的变化、碳水化合物计数错误、小吃和内置胰岛素水平。

结果

该平台通过在大型 CPU 优化机器上的云中解决 ODE 模型，提供了 62%的内存、速度和 CPU 利用率的提高，从而方便了开发。用户可以轻松调试和修改代码、测试多种策略并生成详细的临床性能报告。我们验证并集成了一个用临床数据参数化的普通微分方程 (ODE) 血糖调节系统到平台中，以模拟 15 名 T1DM 患者的血糖日内和日间变化。

结论

该平台利用经过验证的患者模型，对 T1DM 的闭环算法进行快速开发和测试，以进行虚拟临床试验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/9ac31ecca94c/pone.0243139.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/feb2c4b498c5/pone.0243139.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/5689154074e4/pone.0243139.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/7599626ceb60/pone.0243139.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/007628951705/pone.0243139.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/c1c711844b86/pone.0243139.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/bf1442a671c1/pone.0243139.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/13430ca04b4a/pone.0243139.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/756a2735288e/pone.0243139.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/7f76c1b09bc1/pone.0243139.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/9ac31ecca94c/pone.0243139.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/feb2c4b498c5/pone.0243139.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/7599626ceb60/pone.0243139.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/007628951705/pone.0243139.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/c1c711844b86/pone.0243139.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/bf1442a671c1/pone.0243139.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/13430ca04b4a/pone.0243139.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/756a2735288e/pone.0243139.g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b26/7746189/9ac31ecca94c/pone.0243139.g010.jpg

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