SHB: Medium: Collaborative Research: Novel Computational Techniques for Cardiovascular Risk Stratification

SHB：媒介：协作研究：心血管风险分层的新颖计算技术

• 批准号: 1064948
• 负责人: Satinder Baveja
• 金额: $ 56.24万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1064948&HistoricalAwards=false
• 关键词: SHB; Medium; Collaborative; Research; Novel

The project assesses patient cardiovascular risk and matches patients to the treatments most likely to be effective. The project addresses this problem through sophisticated computational methods that identify new markers of disease, improve the ability to measure both new and existing markers, and construct personalized models that can provide highly accurate assessments of individual risk. The core focus of the research addresses the poor performance of existing tools for cardiovascular decision support through advanced methods at the intersection of machine learning, data mining, signal processing, and applied algorithms; with the research guided by knowledge of cardiac pathophysiology.This project impacts patient care for a disease that causes roughly one death every 38 seconds in the United States and imposes a burden of over half a trillion dollars in the U. S. each year. More generally, many of the ideas explored here (e.g., personalization of risk models) extends to a wide variety of other disorders in a straightforward manner and leads to wide improvements in outcomes while controlling costs. The research also strengthens interdisciplinary research in EECs and medicine throughout the computer science research community.

该项目评估患者的心血管风险，并将患者与最有可能有效的治疗相匹配。该项目通过复杂的计算方法解决这个问题，这些方法识别新的疾病标志物，提高衡量新的和现有标志物的能力，并构建能够提供高度准确的个人风险评估的个性化模型。这项研究的核心重点是通过机器学习、数据挖掘、信号处理和应用算法的交叉路口的先进方法，解决现有心血管决策支持工具表现不佳的问题，并在心脏病理生理学知识的指导下进行研究。该项目影响一种疾病的患者护理，在美国，大约每38秒就有一人死亡，每年在美国造成超过5000亿美元的负担。更广泛地说，这里探讨的许多想法(例如，风险模型的个性化)以直截了当的方式扩展到各种其他障碍，并在控制成本的同时导致结果的广泛改善。这项研究还加强了整个计算机科学研究界在EECS和医学方面的跨学科研究。