HPCC TECHNOLOGY FOR REALTIME MEDICAL DECISION SUPPORT

HPCC资源在生物医学上的潜在效益挑战了 想象力，可以影响医学的各个方面，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战略是 实现这些好处。 在学术环境中，HPCC系统将 很可能是从分布在各个业务单位的资源演变而来， 例如，在一个实施例中，部门或研究中心，就像网络一样， 传统的计算资源正在发展。 的程度 现有IAIMS模式和经验适用于有序演进， 然而，高效的管理集成HPCC系统是不确定的，因为 在主要因素，如初始，操作， 行政费用;技术复杂性;用户准入和培训; 具体应用;以及对现有教育， 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，并引导模型发展的方向， 预测而不是对HPCC技术做出反应。 本项目的总体目标 项目是1）证明使用HPCC资源的可行性， 扩大计算机在临床环境中的使用，以访问大量数据 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)评估与纳入HPCC相关的问题和影响 在IAIMS环境中的资源。 具体示范项目， 涉及使用HPCC技术的计算机视觉来决定 是否继续输送每个剂量部分 放射治疗，预计将提供有价值的见解， 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 在需要做出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