A predictive analytics framework for undesired outcomes using vital signs data analysis

使用生命体征数据分析来预测不良结果的预测分析框架

• 批准号: RGPIN-2018-05121
• 负责人: Görges, Matthias
• 金额: $ 1.68万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=652202
• 关键词: predictive; analytics; framework; undesired; outcomes

The long-term goal of this research program is to develop decision support tools that provide predictive information to both clinicians and patients in order to personalize treatments and improve wellness. These tools will: 1) gather and integrate data from electronic warehouses in clinical, personal, and community settings; 2) apply smart algorithms to this data to detect potential outcomes; and 3) distribute this outcome information to electronic health records or smart devices (for clinicians) and smartphone applications (for patients). The short-term objective is to develop and test predictive analytics frameworks that analyze and integrate the multitude of digital signals (both data-banked and real-time) that exist in complex machine-assisted environments to: a) improve data access and exchange between medical devices, while ensuring security/privacy/confidentiality; and b) support the implementation and evaluation of algorithms to predict undesired clinical events using vital signs, medical device, and patient data. ******Importance of research: By facilitating data exchange, applying smart algorithms to predict outcomes from complex physiological and environmental data, and using effective means to visualize and communicate the results, we can make an important contribution to modernizing the tools we need for healthcare. The early detection of the deterioration of critically ill patients before undesired clinical events (e.g., cardiac arrest in the ICU or complications after surgery) is such an application, as it allows for re-assigning priority of care and represents an enormous opportunity to improve outcomes and reduce cost.******Anticipated Outcomes: In collaboration with healthcare organizations, clinicians, data scientists, researchers, and potential industry partners, we will develop two complementary components of the predictive analytics framework: a data exchange component, which obtains and integrates real-time and historic data, using ‘Internet of Things' technologies; and an outcome prediction modeling and processing pipeline, which extracts the data and transforms them into actionable information.******Benefit for Research Field & Canada: Innovation in biomedical and computer engineering is integral to advancing Canadian healthcare. This research program will mitigate barriers in machine-to-machine communication and the meaningful use of such data by providing a framework for safe, trusted, and simple medical data exchange. This framework will be widely applicable in research and development settings. It will underpin the development of smart decision support systems, including predictive analytics models, which will support the reduction of false alarms, closed-loop monitoring/assistive systems and early-warning systems to detect and prevent undesirable health outcomes.

该研究计划的长期目标是开发决策支持工具，为临床医生和患者提供预测信息，以个性化治疗和改善健康。这些工具将：1）从临床、个人和社区环境中的电子仓库收集和整合数据; 2）将智能算法应用于这些数据以检测潜在结果;以及3）将这些结果信息分发到电子健康记录或智能设备（针对临床医生）和智能手机应用程序（针对患者）。短期目标是开发和测试预测分析框架，分析和整合大量数字信号（数据库和实时），以：a）改善医疗设备之间的数据访问和交换，同时确保安全/隐私/保密性;以及B）支持算法的实现和评估，以使用生命体征、医疗设备和患者数据来预测不期望的临床事件。****** 研究的重要性：通过促进数据交换，应用智能算法预测复杂生理和环境数据的结果，并使用有效的手段来可视化和传达结果，我们可以为医疗保健所需工具的现代化做出重要贡献。在发生不良临床事件（例如，ICU中的心脏骤停或手术后并发症）就是这样一种应用，因为它允许重新分配护理优先级，并代表了改善结果和降低成本的巨大机会。预期结果：通过与医疗机构、临床医生、数据科学家、研究人员和潜在的行业合作伙伴合作，我们将开发预测分析框架的两个互补组件：数据交换组件，使用“物联网”技术获取并整合实时和历史数据;以及一个结果预测建模和处理管道，它提取数据并将其转换为可操作的信息。研究领域和加拿大的利益：生物医学和计算机工程的创新是推进加拿大医疗保健不可或缺的。该研究计划将通过提供安全，可信和简单的医疗数据交换框架来减轻机器对机器通信和有意义使用这些数据的障碍。这一框架将广泛适用于研究和开发环境。它将支持智能决策支持系统的开发，包括预测分析模型，这将有助于减少误报，闭环监测/辅助系统和预警系统，以检测和预防不良健康结果。