SCH: EXP: SenseHealth: A Platform to Enable Personalized Healthcare through Context-aware Sensing and Predictive Modeling Using Sensor Streams and Electronic Medical Record Data

SCH：EXP：SenseHealth：使用传感器流和电子病历数据通过情境感知感知和预测建模实现个性化医疗保健的平台

• 批准号: 1344153
• 负责人: Kevin Patrick
• 金额: $ 61.77万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1344153&HistoricalAwards=false
• 关键词: SCH; EXP; SenseHealth; Platform; Enable

Current healthcare diagnostics and assessment systems are limited by health data, which is sporadic, periodic, and incomplete. Wireless devices and health sensor technologies are increasing in use for continuous monitoring and assessment of key physiologic, psychological, and environmental variables and reduce the current gaps in health data. Uptake of such data by current health systems has been slow because of the reliance upon the physician/healthcare team to interpret and manage incoming data. Nevertheless, the large streams of data generated by these devices in conjunction with traditional clinical data (Electronic Medical Records) have the potential provide real and important insights into patient health and behavior. To address this gap, this proposal will develop SenseHealth -- a novel software platform that will automatically process and incorporate volumes of real-time data from sensors tailored to the individual in the context of personal electronic medical records and available environmental data. Such data will be integrated into the clinical care workflow to enable system usability, feasibility, and ultimately utility. A core component of the cyberinfrastructure is a collection of quantitative, predictive models that are sensitive to concerns across age, diseases, and health and variety of patient situations (ranging from low priority with no consequence on patient management to high priority requiring emergency evaluation), and sensor failures. The models will be integrated with a distributed real-time stream data processing system and a complex event stream processing engine to process sensor data in a scalable and fault-tolerant manner. Research at Rady Children's Hospital of San Diego, an affiliate of UCSD will be leveraged to develop these models. In each of the following studies, clinically relevant events (i.e. events that require clinical intervention) will be identified and disease specific models will be developed that will predict clinical relevance or the need for intervention. Incoming data and resulting clinical management activity from studies using various types of health sensors will be evaluated in two different patient populations: (1) MyGlucoHealth application for evaluating the use of a Bluetooth-enabled glucometer (for blood sugar measurements) in 40 youths with Type 1 diabetes, and (2) Asthma Tracking application for evaluating the ability of a metered dose inhaler (MDI) tracking device to track asthma medication use in 50 mild-to-moderate asthma subjects over a period of 6 months. The models will then be evaluated using multiple sensor streams in youth with diabetes (The Diabetes Management Integrated Technology Research Initiative (DMITRI) study) and in a prospective study in youth with asthma to determine their validity, efficacy, and utility in identifying patient scenarios of concern.The SenseHealth system architecture will consist of four major components (1) Health and environmental sensors linked with (2) smartphone applications that communicate with (3) a back-end Data Center comprised of data storage and clusters doing and real-time analytics and data visualization, which will then provide a comprehensive health picture to users/clients via (4) tailored, programmed user/client applications. For these continuous sensing applications, managing sensors and smartphone in an energy-efficient manner is critical. SenseHealth will include a novel context-aware power management framework that uses both the application-level context (e.g., sensor data) and the dynamic environmental or system-level context (e.g., battery level, next phone charging opportunity prediction, or bandwidth availability) to adaptively control the state of hardware components and deliver a consistent performance (e.g. data accuracy, latency). In particular, data sampling protocols will be energy-aware and will be designed to sample data accurately but only as necessary to provide relevant clinical information. SenseHealth will use Storm, an open source distributed real-time computation system to process the data in a scalable and fault-tolerant manner. The aforementioned predictive models will be implemented in ESPER, an open-source complex event processing (CEP) engine. The models will use ESPER's rich Event Processing Language (EPL) to express filtering, aggregation, and joins, possibly over sliding windows of multiple event streams and pattern semantics to express complex temporal causality among events and trigger custom actions when event conditions occur among event streams. Finally, SenseHealth will fuse sensor and clinical data in a visual format that will increase interpretability and comprehension independent of literacy levels and will provide feedback and ultimately intervention support that is timely and relevant to the user (patient and clinician) based on comprehensive knowledge of data. Open source software visualization tools developed at Calit2 that leverage advances in scaled display wall technology will serve as the foundation for the data visualization component. NSF-funded DELPHI project will provide the data center component to store health sensor data and provide access to SenseHealth algorithm-processed data and visualization protocols. The research itself will have direct impact on two patient communities, but the broader impacts of the proposed research will extend well beyond them. The proposed open software platform will be built with flexibility to allow for alternative programming with plug-and-play data processing algorithms as required for various sensors/data sources/clinical scenarios. The results from the proposed development activities and prototyping experiments will be of tremendous value to medical professionals, scientists and engineers who are engaged in planning and developing sensor-based systems for continuous health monitoring. The developed software products will be publicly available as open source products under the Apache license. The tools developed from this proposal will be designed to be extensible so that other sensors as well as models can easily be integrated and impact a broader range of healthcare applications. SenseHealth is an essential step toward providing a real-time 360-degree snapshot of health to optimize patient-centered, evidence-based decisions and to empower patients to participate in their own healthcare. The project team will contribute to training a diverse next generation of scientists by involving undergraduate students in the development process, both for computer science techniques and medical science research. The exciting aspect of this proposed work is that wellness is a very tangible and important factor even at young age. The education program will be structured to excite students, particularly those from traditionally underrepresented groups such as minorities and females, about multi-disciplinary research. Through the UCSD's COSMOS program, simple, fun and hands-on experiences for these students will be designed to allow them to understand importance of self-health assessment and disease management at an early age. The team is involved heavily in Graduate Medical Education at UCSD and will promote use of SenseHealth to integrate health data into current health systems in fellowship training activities. This proposal also funds for one graduate student.

目前的卫生保健诊断和评估系统受到卫生数据的限制，这些数据是零星的、定期的和不完整的。无线设备和健康传感器技术越来越多地用于持续监测和评估关键的生理、心理和环境变量，并缩小目前健康数据方面的差距。由于依赖医生/医疗保健团队来解释和管理传入的数据，目前的卫生系统对此类数据的吸收速度很慢。尽管如此，这些设备产生的大量数据流与传统的临床数据（电子医疗记录）相结合，有可能为患者的健康和行为提供真实而重要的见解。为了解决这一差距，该提案将开发SenseHealth，这是一个新颖的软件平台，可以自动处理和整合来自个人电子病历和可用环境数据背景下为个人量身定制的传感器的大量实时数据。这些数据将被整合到临床护理工作流程中，以确保系统的可用性、可行性和最终的实用性。网络基础设施的一个核心组成部分是一组定量预测模型，这些模型对年龄、疾病和健康以及各种患者情况（从对患者管理没有影响的低优先级到需要紧急评估的高优先级）和传感器故障敏感。这些模型将与分布式实时流数据处理系统和复杂事件流处理引擎集成，以可扩展和容错的方式处理传感器数据。加州大学圣地亚哥分校附属机构圣地亚哥雷迪儿童医院的研究将用于开发这些模型。在以下每一项研究中，将确定临床相关事件（即需要临床干预的事件），并开发疾病特定模型，以预测临床相关性或干预需求。将在两种不同的患者群体中评估使用各种健康传感器的研究获得的数据和由此产生的临床管理活动：(1) MyGlucoHealth应用程序，用于评估40名青少年1型糖尿病患者使用蓝牙血糖仪（用于血糖测量）的情况；(2)Asthma Tracking应用程序，用于评估计量吸入器（MDI）跟踪设备跟踪50名轻度至中度哮喘患者6个月哮喘药物使用情况的能力。然后，这些模型将在青少年糖尿病患者（糖尿病管理综合技术研究计划（DMITRI）研究）和青少年哮喘患者的前瞻性研究中使用多个传感器流进行评估，以确定它们在识别患者关注情景方面的有效性、有效性和实用性。SenseHealth系统架构将由四个主要组件组成：(1)与(2)与(3)通信的智能手机应用程序连接的健康和环境传感器，(3)由数据存储和集群组成的后端数据中心，进行实时分析和数据可视化，然后通过(4)定制的、可编程的用户/客户端应用程序向用户/客户提供全面的健康图片。对于这些连续传感应用，以节能的方式管理传感器和智能手机至关重要。SenseHealth将包括一个新颖的上下文感知电源管理框架，该框架使用应用级上下文（例如，传感器数据）和动态环境或系统级上下文（例如，电池电量，下一次手机充电机会预测或带宽可用性）来自适应地控制硬件组件的状态并提供一致的性能（例如，数据准确性，延迟）。特别是，数据采样方案将具有能量意识，并将设计为准确采样数据，但仅在必要时提供相关临床信息。SenseHealth将使用开源分布式实时计算系统Storm，以可扩展和容错的方式处理数据。上述预测模型将在开源复杂事件处理（CEP）引擎ESPER中实现。这些模型将使用ESPER丰富的事件处理语言（EPL）来表达过滤、聚合和连接，可能通过多个事件流的滑动窗口和模式语义来表达事件之间复杂的时间因果关系，并在事件流中发生事件条件时触发自定义动作。最后，SenseHealth将以视觉形式融合传感器和临床数据，这将提高可解释性和可理解性，而不受文化水平的影响，并将根据全面的数据知识，为用户（患者和临床医生）提供及时和相关的反馈和最终干预支持。Calit2开发的开源软件可视化工具利用了先进的缩放显示墙技术，将作为数据可视化组件的基础。nsf资助的DELPHI项目将提供数据中心组件来存储健康传感器数据，并提供对SenseHealth算法处理数据和可视化协议的访问。这项研究本身将对两个患者群体产生直接影响，但拟议研究的更广泛影响将远远超出这两个群体。拟议的开放式软件平台将具有灵活性，允许根据各种传感器/数据源/临床场景所需的即插即用数据处理算法进行替代编程。拟议的开发活动和原型实验的结果将对从事规划和开发基于传感器的持续健康监测系统的医学专业人员、科学家和工程师具有巨大价值。开发的软件产品将在Apache许可下作为开源产品公开提供。根据该建议开发的工具将被设计为可扩展的，以便可以轻松集成其他传感器和模型，并影响更广泛的医疗保健应用程序。SenseHealth是提供实时360度健康快照的重要一步，以优化以患者为中心的循证决策，并使患者能够参与自己的医疗保健。项目团队将通过让本科生参与计算机科学技术和医学科学研究的开发过程，为培养多样化的下一代科学家做出贡献。这项提议的工作令人兴奋的方面是，健康是一个非常切实和重要的因素，即使在年轻的时候。该教育项目的结构将激发学生对多学科研究的兴趣，尤其是那些来自少数民族和女性等传统上代表性不足的群体的学生。通过加州大学圣地亚哥分校的COSMOS项目，将为这些学生设计简单、有趣和实际操作的体验，让他们在早期就了解自我健康评估和疾病管理的重要性。该团队大力参与加州大学圣地亚哥分校的研究生医学教育，并将在奖学金培训活动中推广SenseHealth的使用，将健康数据整合到当前的卫生系统中。该提案还资助了一名研究生。