Bio-medical Ontologies Maintenance and Change Management

Completing the Total Wellbeing Puzzle Using a
Multi-agent System
Maja Hadzic 1
, Meifania Chen 1
, and Rick Brouwer 2
1
Curtin University of Technology, Digital Ecosystems and Business Intelligence Institute
(DEBII)
GPO Box U1987 Perth,
Western Australia 6845, Australia
2
Total Wellbeing Medical and Counseling Centre
Suite 1 / 857 Doncaster Rd
Doncaster East 3109
Victoria, Australia
{m.hadzic,m.chen}@curtin.edu.au, rickb@totalwellbeing.com.au
Abstract. Our research focus is the implementation of agent-based systems within
the health domain, more specifically, in the study of total wellbeing. We use an
evidence-based total wellbeing ontological model where the total wellbeing is
seen as a function of physical health, mental health, emotions, relationships, financial
situation and spirituality. We use the TICSA methodology to design a
multi-agent system. This multi-agent system is based on the Total Wellbeing Ontology
and helps intelligent retrieval, management and analysis of information related
to total wellbeing. We hope this system to expose evidence that will support
general public in managing their personal wellbeing better, and health professionals
in adapting their services to address patients’ needs more systematically and
effectively.
Keywords: Total Wellbeing, Ontology-based Multi-agent System, Multi-agent
System Design, e-Health, Health Information System.
1 Introduction
The main features of agents are their autonomous, goal-driven, intelligent, proactive,
cooperative, collaborative and mobile capabilities. Agents have the ability to
act independently from both the user and the rest of the system. They are usually
driven by a specific goal which benefits the whole system. Agents are equipped
with intelligence that enables them to reason and perform the beneficial actions.
Agents are proactive, namely, are able to make decisions and take action on their
own initiative.
Even though the agent is able to act autonomously, it has to be sociable; it
needs to cooperate and collaborate with other agents of the system. The performance
of multi-agent systems is based on collaborative effort of various agent types.
The different agents work cooperatively towards a shared goal. Multi-agent systems
are designed to reach their full potential through cooperation, coordination of
their actions, tasks and results sharing. The collaborative nature of agents enables
A.S. Sidhu et al. (Eds.): Biomedical Data and Applications, SCI 224, pp. 281–293.
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