Integration of healthcare information

在做出医疗决策时，及时获取相关临床信息至关重要。电子健康记录（EHR）通过提供对构成患者医疗记录的不同数据源的集中访问，承诺提供更好的护理。这些数据包括患者病史、来自各种护理领域的诊断报告、图像和其他电子格式的临床数据。除了能够及时访问相关临床信息外，EHR还为协助临床任务和指导医疗决策创造了全新的机会。随着对源自各种来源的信息的访问变得可能，可以从信息集成中增强护理决策。信息集成的目的是通过集成从不同来源收集的数据中提取的信息来获得统一的综合视图。提取的信息需要在语义上进行整合、组织和呈现，以帮助临床研究。另一方面，随着EHR的出现，数据安全问题变得越来越重要。需要通过控制对患者电子同意书所表示的个人健康信息的访问来确保保密。在拟议的研究计划的框架内，我们将探索一种新的访问控制框架的基础上，患者的同意。我们还将探索信息整合，组织和呈现的新方法，以帮助信息导航。此外，我们将研究创新的方法来识别和定位多模态图像，如X射线和磁共振图像的特征，以协助乳腺癌评估。

Prompt access to relevant clinical information is crucial when making healthcare decisions. The Electronic Health Record (EHR) promises better care by providing centralized access to different sources of data that make up the patient medical record. Such data includes the patient medical history, diagnostic reports from various care domains, images and other clinical data in electronic format. In addition to enabling timely access to relevant clinical information, EHR creates totally new opportunities for assisting in clinical tasks and for guiding medical decisions. As access to information originating from various sources is becoming possible, care decisions can be enhanced from information integration. Information integration aims to obtain a unified comprehensive view by integrating information that is extracted from data gathered at various sources. The extracted information needs to be semantically integrated, organized, and presented in order to help in the clinical investigations. On the other hand, with the EHR, the issue of data security has become increasingly important. Confidentiality needs to be ensured by controlling the access to personal health information as expressed by the patients' electronic consent. Within the framework of the proposed research program, we will explore a new access control framework based on the patients consent. We will also explore novel approaches for information integration, organization and presentation to help in information navigation. Furthermore, we will investigate innovative methods to identify and co-locate features from multi-modality images, such as X-rays and Magnetic Resonance Images, in order to assist in breast cancer assessment.