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

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方法相匹配。该项目通过复杂的计算方法来解决这个问题，这些方法可以识别新的疾病标记，提高测量新的和现有标记的能力，并构建能够提供高度准确的个人风险评估的个性化模型。该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点的先进方法解决现有心血管决策支持工具的不良性能；以心脏病理生理学知识为指导进行研究。这个项目影响了美国每38秒就有一人死亡的疾病的病人护理，每年给美国带来超过5000亿美元的负担。更一般地说，这里探讨的许多想法（例如，风险模型的个性化）以一种直接的方式扩展到各种其他疾病，并在控制成本的同时导致结果的广泛改善。该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。

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.