A Semantic Web Primer
Guest Editorial: Semantic mashup of biomedical data
Journal of Biomedical Informatics
An ontology-based collaborative design system
CDVE'09 Proceedings of the 6th international conference on Cooperative design, visualization, and engineering
OntoMetaWorkflow: an ontology for representing data and users in workflows
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Integrating clinical pathways into CDSS using context and rules: a case study in heart disease
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
A standard language for service delivery: Enabling understanding among stakeholders
Computer Standards & Interfaces
Collaboration-based medical knowledge recommendation
Artificial Intelligence in Medicine
Detecting dominant alternative interventions to reduce treatment costs
KR4HC'11 Proceedings of the 3rd international conference on Knowledge Representation for Health-Care
Semantic annotation of image processing tools
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Journal of Biomedical Informatics
Semantic agent system for automatic mobilization of distributed and heterogeneous resources
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
Ontological model for CDSS in knee injury management
UAHCI'13 Proceedings of the 7th international conference on Universal Access in Human-Computer Interaction: applications and services for quality of life - Volume Part III
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As emerging technologies, semantic Web and SOA (Service-Oriented Architecture) allow BPMS (Business Process Management System) to automate business processes that can be described as services, which in turn can be used to wrap existing enterprise applications. BPMS provides tools and methodologies to compose Web services that can be executed as business processes and monitored by BPM (Business Process Management) consoles. Ontologies are a formal declarative knowledge representation model. It provides a foundation upon which machine understandable knowledge can be obtained, and as a result, it makes machine intelligence possible. Healthcare systems can adopt these technologies to make them ubiquitous, adaptive, and intelligent, and then serve patients better. This paper presents an ontological knowledge framework that covers healthcare domains that a hospital encompasses-from the medical or administrative tasks, to hospital assets, medical insurances, patient records, drugs, and regulations. Therefore, our ontology makes our vision of personalized healthcare possible by capturing all necessary knowledge for a complex personalized healthcare scenario involving patient care, insurance policies, and drug prescriptions, and compliances. For example, our ontology facilitates a workflow management system to allow users, from physicians to administrative assistants, to manage, even create context-aware new medical workflows and execute them on-the-fly.