课题基金 / 基金详情

OBJECTIFICATION OF MEDICAL FINDINGS--INDIVIDUAL IMAGING PROTOCOLS

OBJECTIFICATION OF MEDICAL FINDINGS--INDIVIDUAL IMAGING PROTOCOLS
医学结果的客观化——个体成像方案
批准号:
6338334
负责人:
HOOSHANG KANGARLOO
金额:
$24.88万
依托单位国家:
美国
项目类别:
财政年份:
1990
资助国家:
美国
项目状态:
已结题
起止时间:
1990-05-01 至 2005-03-31

项目摘要

项目成果

HOOSHANG KANGARLOO的其他基金

相关文献

中文摘要
翻译
本提案的广泛、长期目标是建立一个具有以下特征的医疗保健提供系统:(1)专注于成像,促进客观化、循证医学实践,(2)能够定制适当的成像协议,为个体患者定制适当的成像协议,以及(3)将远程放射学转换为在各种硬件平台上运行的基础设施。该提案的具体目标是通过基于专家和结构化患者数据的输入定义个性化定制的成像检查序列来客观化患者的主观临床表现,开发自动数据收集方法以促进与过程,结果和资源利用相关的健康服务研究,开发将患者数据与医学文献相关联的算法,设计一个模块化系统,实现硬件独立性,并实施和评估具有明确定义的患者人群的原型。该项目对循证医学实践的促进应减少医疗不确定性并提高护理质量。研究设计和方法包括:(1)结构化数据输入,远程咨询和监督机器学习,用于个性化定制的成像协议;(2)用于健康服务数据收集的灵活输入架构和数据表示;(3)特征提取,模式发现和基于内容的索引技术,用于内容相关性;(4)以网络为中心的体系结构,它使用TCP/IP、JAVA和CORBA标准以实现硬件独立性。发展拟议的基础设施将促进以实证为基础的医学实践,改善护理过程和结果,并通过提供更准确的医疗服务研究数据来促进成本效益研究。
英文摘要
The broad, long-term objective of this proposal a health care delivery system with the following characteristics: (1) a focus on imaging that facilitates an objectified, evidence-based practice of medicine, (2) the ability to tailor an appropriate imaging protocol to tailor an appropriate imaging protocol for an individual patient, and (3) transform teleradiology to an infrastructure that runs on a variety of hardware platforms. The proposal specifically aims to objectify a patient's subjective clinical presentation by defining individually tailored imaging work-up sequences based on input from specialists and structured patient data, develop methods for automated data collection to facilitate health services research related to the process, outcomes, and resource utilization, develop algorithms to correlate patient data with medical literature, design a modular system that enables hardware independence and implement and evaluate a prototype with a well-defined patient population. The project's facilitation of evidence-based practice of medicine should reduce medical uncertainties and improve quality of care. Research design and methods include: (1) structured data input, teleconsultation, and supervised machine learning for individually tailored imaging protocols; (2) a flexible input architecture and data representation for health services data collection; (3) feature extraction, pattern discovery, and content-based indexing techniques, for content correlations; and (4) a network-centric architecture which use TCP/IP, JAVA, and CORBA standards for hardware independence. Development of the proposed infrastructure will promote evidence-based practice of medicine, improve the process and outcome of care, and facilitate cost-effectiveness studies by providing more accurate health services research data.
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