HPCC TECHNOLOGY FOR REALTIME MEDICAL DECISION SUPPORT

HPCC资源在生物医学中的潜在好处挑战 想象力，可以影响医学的方方面面，包括教育 以及培训、管理和临床实践。的发展。 有效的资源整合和管理策略是 实现这些好处。在学术环境中，HPCC系统将 可能从分布在各个操作单元中的资源演变而来， 例如，部门或研究中心，其方式与网络基本相同 的传统计算资源现在正在演变。在多大程度上 目前的IAIMS模式和经验适用于有序进化和 然而，集成HPCC系统的有效管理是不确定的，因为 主要因素的显著差异，如初始、运行和 管理成本；技术复杂性；用户访问和培训； 具体应用；以及对现有教育的总体影响， 研究和医疗系统。重点突出的HPCC示范项目 IAIMS环境可以提供有关适用性的有价值的数据 现有模型，并将模型开发引向 预测而不是对HPCC技术做出反应。这一行动的总体目标是 项目的目的是：1)论证利用HPCC资源的可行性 扩大临床环境中计算机的使用，以访问大数据 银行和执行大型数据集的复杂分析，如医疗 图像，用于现场、实时支持关键医疗决策，以及 2)评估与合并HPCC相关的问题和影响 IAIMS设置中的资源。具体的示范项目， 涉及到使用计算机视觉的HPCC技术来决定 是否继续递送每个剂量分数的 放射疗法，预计将提供有价值的洞察 具有一般性的技术、管理和相关问题 对其他可能受益于HPCC的临床应用的意义 在需要关键医疗决策时提供现场支持的技术

The potential benefits of HPCC resource for biomedicine challenge the imagination and can effect every aspect of medicine, including education and training, administration, and clinical practice. The development of effective resource integration and management strategies is key to the realization of these benefits. In academic settings, HPCC systems will likely evolve from resources distributed in individual operational units, e.g., departments or research centers, in much the same way that networks of conventional computing resources are evolving now. The extent to which current IAIMS model and experiences are applicable to orderly evolution and efficient management integrated HPCC systems is uncertain however because of significant differences in major factors such as initial, operating, and administrative cost; technological complexity; user access and training; specific applications; and overall impact on existing educational, research, and medical systems. Well focused HPCC demonstration projects in an IAIMS environment can provide valuable data regarding the applicability of existing models, and steer model development in directions that anticipate rather than react to HPCC technology. The overall aims of this project are to 1) demonstrate the feasibility of using HPCC resources to extend the use of computers in the clinical setting to access large data banks and to perform complex analyses of large data sets, such as medical images, for on-site, real-time support of critical medical decisions, and 2) evaluate issues and implications associated with incorporating HPCC resources in an IAIMS setting. The specific demonstration project, which involves the use of both computer vision of HPCC technology for deciding whether or not to proceed with the delivery of each dose fraction of radiation therapy, is expected to provide valuable insight into the technological, administrative, and related issues that are of general significance to other clinical applications that could benefit from HPCC technology for-on-site support when critical medical decisions are required