Development of a distributed intelligent decision support system

分布式智能决策支持系统的开发

• 批准号: 202972-2010
• 负责人: Frize, Monique
• 金额: $ 1.82万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Group
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=523314
• 关键词: Development; distributed; intelligent; decision; support

Today, no technique does well in predicting the occurrence of a premature birth. We intend to integrate various data mining techniques to increase the accuracy of the prediction of the following outcomes: premature birth, delivery type, and status of the infant at birth. We plan to study three clinical databases to assess which factors best predict these outcomes. The proposed work includes the development of an electronic patient record (EPR) capable of handling the multiple types of information (data, images, etc..) and define a multimedia model adapted to current medical practice in obstetrics and perinatal care. The EPR will be integrated to an improved and expanded decision-support system (DSS). We will implement standards-driven, open and extensible plug-in system architecture; this will seamlessly analyse patient-specific data and information to deliver personalised healthcare. Results stored in the EPR, along with response to treatments and patient prognosis will offer a unique clinical practice tool not available in current research or commercial systems. The final stage is to develop a web services-based infrastructure to enable easy, but secure access to the DSS and to the databases from local and remote locations and by several users at points in time when the information is needed. The prototype will undergo a short pilot test in a clinical setting at the Ottawa Hospital and at the Isaac Walton Killam Hospital in Halifax. Physicians will be able to intervene earlier and improve patient outcomes. Using our system for screening, the costly invasive test (fibronectin) could now be done mainly for patients whose risk estimation is high. Several students will be trained in an area of high demand, in the multidisciplinary environment of engineering, computer science, and medicine.

今天，没有一种技术能很好地预测早产的发生。我们打算集成各种数据挖掘技术来提高对以下结果的预测的准确性：早产、分娩类型和婴儿出生时的状态。我们计划研究三个临床数据库，以评估哪些因素最能预测这些结果。拟议的工作包括开发能够处理多种类型的信息(数据、图像等)的电子病历(EPR)。并定义了适应当前产科和围产期医疗实践的多媒体模型。EPR将纳入一个改进和扩大的决策支持系统(DSS)。我们将实施标准驱动、开放和可扩展的插件系统架构；这将无缝地分析患者特定的数据和信息，以提供个性化的医疗保健。存储在EPR中的结果，以及对治疗的反应和患者的预后，将提供当前研究或商业系统无法提供的独特临床实践工具。最后一个阶段是开发基于网络服务的基础设施，以便在需要信息时，能够从本地和远程地点以及若干用户方便而安全地访问决策支持系统和数据库。该原型将在渥太华医院和哈利法克斯的艾萨克·沃尔顿·基拉姆医院进行简短的临床试验。医生将能够更早地进行干预，改善患者的预后。使用我们的系统进行筛查，昂贵的侵入性测试(纤维连接蛋白)现在可以主要针对风险估计较高的患者进行。几名学生将在工程、计算机科学和医学的多学科环境中接受高需求领域的培训。