Use of ontology-based multi-agent systems in the biomedical domain

Coordination, cooperation and exchange of information is important to the medical community. We design a new ontology, called Generic Human Disease Ontology (GHDO), by merging and aligning existing medical ontologies. The concepts of the GHDO are organized into the following four dimensions: Types, Symptoms, Causes and Treatments of human diseases. We also design a multi-agent system framework over different information resources. The multi-agent system uses the common GHDO ontology for query formulation, information retrieval and information integration. We conclude that this intelligent dynamic system provides opportunities to collect information from multiple information resources, to share data efficiently and to integrate and manage scientific results in a timely manner

Use and modeling of multi-agent systems in medicine

Multi-Agent System (MAS), and more specifically, ontology-based MAS, are increasingly being proposed and used within the medical domain. In this paper we represent an ontology-based multi-agent system specifically designed to intelligently retrieve information about human diseases. Thehuman disease ontology is organized according to the four dimensions: disease types, symptoms, causes and treatments. The multi-agent system consists of four different types of agent: Interface, Manger, Information and Smart agent. We use of UML 2.1 to model social and goal-driven nature of agents. We believe that UML 2.1 has not only provided a way for standardized notation of MAS, but also for effective representation of the dynamic processes associated with these MAS

User Customisation of Agent Profiles in the National electronic Library for Communicable Disease

The Internet provides overwhelming amount of medical information. However, healthcare professionals often cannot find the information they need when they need it and if they do the quality may be uncertain. A new Internet digital library, the National electronic Library for Communicable Disease (NeLCD), is addressing this issue by providing a single-entry portal to evidence-based information on treatment, investigation and prevention of communicable disease. Autonomous Intelligent Agents are essential for the development and runtime of the NeLCD library as they perform autonomously a number of tasks related to the search, assist humans in information publishing, the document review process and data exchange. In this paper, we present an application of Intelligent Agents in user profiling and customisation. In particular, they allow users to personalise the search, modify the input controlled vocabulary and customize the search results to better meet their needs. In addition, they can autonomously alert users about new postings according to their interests. Profiling of Intelligent Search Agents (ISA) and Pro-active Alert Agents (PAA) allows extensive customisation of the library according to user’s personal preferences, professional background and medical specialty

Knowledge Management and Communities of Practice around Healthcare Digital Libraries

The recent explosion of medical information available in digital libraries on the Internet provides users with overwhelming amount of medical knowledge. Although the number of patients seeking health related information online is steadily growing, the great potential of this revolutionary technology has not been fully exploited. Professionals often cannot find information when and where they need it; members of public are unaware of varying quality of medical information and often seek health advice from unauthorized and misleading Web sites. In addition, little is known about the real impact of medical knowledge provision on clinical care. Based on our experience with the development of real-world government medical digital libraries in the UK (NeLI and AR DL), we will discuss key issues around knowledge management, healthcare ontologies, quality approval and a new opportunity for online communities of practice around healthcare digital libraries

A requirements elicitation framework for agent-oriented software engineering.

The hypothesis of this research is as follows: "Conceptual modelling is a useful activity for the early part of gathering requirements for agent-based systems." This thesis examines the difficulties of gathering and expressing requirements for agent based systems, and describes the development of a requirements elicitation framework. Conceptual modelling in the form of Conceptual Graphs is offered as a means of representing the constituent parts of an agent-based system. In particular, use of a specific graph, the Transaction Model, illustrates how complex agent concepts can be modelled and tested prior to detailed design specification, by utilising a design metaphor for an organisational activity.Using an exemplar in the healthcare domain, a preliminary design framework is developed showing how the Transaction Agent Modelling (TrAM) approach assisted the design of complex community healthcare payment models. Insight gained during the design process is used to enrich and refine the framework in order that detailed ontological specifications can be constructed, before validating with a mobile learning scenario. The ensuing discussion evaluates how useful the approach is, and demonstrates the following contributions: Use of the Transaction Model to impose a rigour upon the requirements elicitation process for agent-based systems; Use of Conceptual Graph type hierarchies for ontology construction; A means to check the transaction models using graphical inferencing with Peirce Logic; Provision of a method for the elicitation and decomposition of soft goals; The TrAM process for agent system requirements elicitation