SHB: Type II (INT): Collaborative Research: Algorithmic Approaches to Personalized Health Care

SHB：II 类 (INT)：协作研究：个性化医疗保健的算法方法

• 批准号: 1237022
• 负责人: Ioannis Paschalidis
• 金额: $ 110万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1237022&HistoricalAwards=false
• 关键词: SHB; Type; II; INT; Collaborative

The US health care system spends major resources on the treatment of acute conditions in a hospital setting rather than focusing on prevention and keeping patients out of the hospital. While there is no broad agreement on the potential solutions, structural reform is on the horizon. The meaningful use of Electronic Health Records (EHRs) is seen as a key to improving efficiency. Technology solutions including miniaturized implantable medical devices, networked home monitoring devices, and the ubiquitous use of smart phones are starting to enable continuous real-time monitoring of patients as they go about their daily lives. Rich data from these devices form an electronic Personal Health Record (PHR) that captures patient health in a much finer time-scale than the EHR. The health care system, however, is not well equipped to benefit from the impending deluge of personalized health-related data in order to improve health outcomes and reduce costs. This proposal puts forth a comprehensive and systematic approach to intelligently process such data aiming at preventing hospitalization, empowering patients to actively participate in managing their health, assessing quality of care, and facilitating cost-effective epidemiology in the emerging data-rich environment. In the proposed framework, early risk assessment starts with algorithms for mining EHR and PHR data to classify patients in terms of the risk they have for developing an acute condition that would require hospitalization and/or incur large costs. Risk stratification produced by our approach triggers a set of actions, including tests, additional and more intensive monitoring, and physician involvement. As part of this dynamic health management process, patients can have access to tools that enable their active participation in the daily management of chronic conditions, such as diabetes. Our plans include the development of algorithms that leverage experts? opinions to assess quality of care and the development of a distributed epidemiology approach suitable for the emerging landscape where lots of data about each patient are distributed among many different locations.The proposed work has the potential to achieve revolutionary improvements in the quality of health care. Risk assessment combined with intelligent management of chronic conditions can prevent acute health episodes and dramatically improve health outcomes. Having rigorous and scalable ways of assessing the quality of care has the potential to reduce medical errors and improve coordination among health care providers. On the educational front, plans include new courses, training a diverse set of graduate students, involving undergraduate students, actively collaborating with medical doctors, and reaching out to high school students through existing programs embraced by the PIs. Dissemination plans include capitalizing on the BU Sensor Network Consortium and organizing a major medical informatics workshop.

美国医疗保健系统将主要资源用于医院环境中的急性疾病治疗，而不是将重点放在预防和让患者远离医院上。虽然对于可能的解决方案还没有达成广泛的共识，但结构性改革已经指日可待。有效使用电子健康记录（EHRs）被视为提高效率的关键。包括微型植入式医疗设备、联网家庭监控设备和无处不在的智能手机在内的技术解决方案开始使患者在日常生活中进行持续实时监控成为可能。来自这些设备的丰富数据形成电子个人健康记录（PHR），以比EHR更精确的时间尺度捕捉患者的健康状况。然而，医疗保健系统还没有做好准备，无法从即将到来的个性化健康相关数据洪流中获益，从而改善健康结果并降低成本。该提案提出了一种全面和系统的方法来智能处理这些数据，旨在预防住院，使患者能够积极参与管理自己的健康，评估护理质量，并在新兴的数据丰富的环境中促进具有成本效益的流行病学。在提议的框架中，早期风险评估从挖掘EHR和PHR数据的算法开始，根据患者发展为需要住院治疗和/或产生大笔费用的急性疾病的风险对患者进行分类。我们的方法产生的风险分层触发了一系列行动，包括测试、额外和更密集的监测以及医生的参与。作为这一动态健康管理过程的一部分，患者可以使用工具，使他们能够积极参与糖尿病等慢性疾病的日常管理。我们的计划包括开发利用专家的算法。评估护理质量的意见和分布式流行病学方法的发展，适合于每个患者的大量数据分布在许多不同地点的新兴情况。拟议的工作有可能在保健质量方面实现革命性的改进。风险评估与慢性病的智能管理相结合，可以预防急性健康发作，并显著改善健康结果。拥有严格和可扩展的评估护理质量的方法，有可能减少医疗差错并改善卫生保健提供者之间的协调。在教育方面，计划包括开设新课程，培养多样化的研究生，包括本科生，积极与医生合作，并通过pi接受的现有项目向高中生伸出援助之手。传播计划包括利用波士顿大学传感器网络联盟和组织一次大型医学信息学讲习班。

项目成果

Botnet Detection Based on Anomaly and Community Detection

• DOI: 10.1109/tcns.2016.2532804
• 发表时间: 2017-06
• 期刊: IEEE Transactions on Control of Network Systems
• 影响因子: 4.2
• 作者: Jing Wang;I. Paschalidis

Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models

• DOI: 10.1109/tsp.2017.2771722
• 发表时间: 2017-02
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Jing Zhang;I. Paschalidis

An Actor-Critic Algorithm With Second-Order Actor and Critic

一种具有二阶 Actor 和 Critic 的 Actor-Critic 算法

• DOI: 10.1109/tac.2016.2616384
• 发表时间: 2017
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Wang, Jing;Paschalidis, Ioannis Ch.
• 通讯作者: Paschalidis, Ioannis Ch.