Vast amounts of clinical and biomedical research data are produced daily. These data can help enable data driven healthcare through novel biomedical discoveries, improved diagnostics processes, epidemiology, and education. However, finding, and gaining access to these data and relevant metadata that are necessary to achieve these goals remains a challenge. Furthermore, data management and enabling widespread, albeit controlled, use poses a major challenge for data producers. These data sources are often geographically distributed, with diverse characteristics, and are controlled by a host of logistical and legal factors that require appropriate governance and access control guarantees. To overcome these obstacles, a set of guiding principles under the term FAIR has been previously introduced. The primary desirable dataset properties are thus that the data should be Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we introduce and describe an abstract framework that models these ideal goals, and could be a step toward supporting data driven research. We also develop a system instantiated on our framework called the Data integration and indexing System (DiiS). The system provides an integration model for making healthcare data available on a global scale. Our research work describes the challenges inhibiting data producers, data stewards, and data brokers in achieving FAIR goals for sharing biomedical data. We attempt to address some of the key challenges through the proposed system. We evaluated our framework using the software architecture testing technique and also looked at how different challenges in data integration are addressed by our system. Our evaluation shows that the DiiS framework is a user friendly data integration system that would greatly contribute to biomedical research.
Personalized Biomedical Data Integration
