Semantic Framework in Healthcare

Summary

Healthcare applications are diverse in nature and they are in critical need of compatible and understandable systems through which they can communicate effectively. A semantic framework for such disparate applications is very necessary so that the resultant seamless communication between them would boost up the patient information access and healthcare improvement processes. Semantic interoperability among various healthcare data sources is a challenging task for efficient healthcare delivery within and across communities. Semantic Web is introduced for extracting meaningful contents from different kinds of healthcare data. It is followed by multiple agent systems that help in regularizing the discovery of information from such curated data. A primitive multiple agent system that enhances the semantic data retrieval and translation process is explained in this chapter. By leveraging Resource Description Framework (RDF) as the information representation medium between the different data models to achieve semantic interoperability, structured mapping definitions and ontologies for Semantic Web Services and decision making, a translation system for healthcare instance data is proposed. Different agents employed in this framework assist the users in invoking the required services and monitor the system performance. This system finds out the linkages between data model elements aimed at improved accuracy in healthcare instance matching.