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CAREER: Engineering Data-intensive Prediction and Classification for Medical Testbeds with Nonlinear, Distributed, and Interdisciplinary Approaches

职业：采用非线性、分布式和跨学科方法对医学试验台进行工程数据密集型预测和分类

基本信息

批准号：
1054333
负责人：
Yuichi Motai
金额：
$ 40万
依托单位：
Virginia Commonwealth University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2011
资助国家：
美国
起止时间：
2011-02-01 至 2018-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1054333&HistoricalAwards=false
关键词：
CAREER Engineering Data intensive Prediction

项目摘要

ABSTRACTResearch Objectives and ApproachesThe objective of this research is to contribute to the interdisciplinary topic on STEM and basic medical science, specifically Patient-Centered Health Informatics Applications, with the newly proposed techniques that stand to benefit from the investigator?s expertise in engineering and from the collaboration with medical experts.The approach is to study the medical data from several institutions comprehensively with the dynamics of all the datasets in their entirety such as non-linearity with kernel factors, and the network characteristics of whole multiple database distribution, rather than applying traditional techniques of prediction and classification to the very limited number of small medical testbeds.Intellectual Merit When the proposed adaptive tracking method is used on soft tissue tumors, radiosurgery systems maintain precise targeting of the tumor by predicting tumor motion using a motion tracking system. The successful development of the proposed dynamic classification method will substantially advance the clinical implementation of cancer screening, promote the early diagnosis of colon cancer, lead to an improved screening rate, and ultimately contribute toward reducing the mortality due to colon cancer.Broader impacts The proposed data-intensive solutions can save millions of cancer patients every year. The expected outcomes will be applied to medical problems and benefit society as a whole by enhancing the quality of all our lives, through unprecedented advances in the early diagnosis and treatment of cancer. The advancements in the developed framework will make use of and expand the Nation's cyber infrastructure and high performance computing capability.

摘要研究目标和方法本研究的目的是促进STEM和基础医学科学的跨学科主题，特别是以患者为中心的健康信息学应用，与新提出的技术，站在受益于调查？该方法是综合研究来自多个机构的医学数据，研究所有数据集的整体动态性，如具有核因子的非线性，以及整个多数据库分布的网络特征，而不是将传统的预测和分类技术应用于数量非常有限的小型医疗试验台。所提出的自适应跟踪方法用于软组织肿瘤，放射外科系统通过使用运动跟踪系统预测肿瘤运动来保持肿瘤的精确靶向。该动态分类方法的成功开发将大大推进癌症筛查的临床实施，促进结肠癌的早期诊断，提高筛查率，并最终有助于降低结肠癌的死亡率。更广泛的影响所提出的数据密集型解决方案每年可挽救数百万癌症患者的生命。预期成果将应用于医疗问题，并通过在癌症早期诊断和治疗方面取得前所未有的进展，提高我们所有人的生活质量，造福整个社会。在开发框架的进步将利用和扩大国家的网络基础设施和高性能计算能力。