Bio-medical Ontologies Maintenance and Change Management

Completing the Total Wellbeing Puzzle Using a Multi-agent System 283
Organization-based multi-agent systems are designed for to be used within a
specific institution. Usually, these systems help manage and use the existing information.
For example, Agent Cities [3] is a multi-agent system that consists of
agents that provide medical services. The agents enable the user to access his/her
medical record, to make a booking to be visited by a particular kind of doctor and
to search for medical centres on the basis of a given set of requirements. AADCare
[4] agent-based system is designed as a decision support system for physicians. It
has the ability to map patient’s records to the predefined domain knowledge such as
disease knowledge, clinical management plans, patient records etc.
Other multi-agent systems retrieve information from the Internet. BioAgent [5]
is a mobile agent system where each agent is associated with a given task. For
successful completion of its task, the agent needs to travel among multiple locations.
At each location, the agent performs a set of actions. Information integration
procedure takes place at the end of the trip. Holonic Medical Diagnostic System
(HMDS) [6] architecture is a medical diagnostic system i.e. Internet-based diagnostic
system for diseases. It is based on the holonic paradigm, multi-agent system
technology and swarm intelligence. The patient’s information is kept in comprehensive
computer readable patient record called computer readable patient pattern
(CRPP). The agents of the holarchy use the information available via Internet to
processed CRPP. Another work describes web crawling agents [7] that can be designed
to fetch information about diseases on the basis of the information about
mutated genes related to these diseases.
Use of the ontologies within biomedical communities is of great significance
[8]. The Gene Ontology (GO) (http://www.geneontology.org/) has been used to
annotate major repositories for plant, animal and microbial genomes. This has
resulted in consistent descriptions of gene products in different databases. The
Unified Medical Language System (UMLS) [8] contains 1 million biomedical
concepts, 135 semantic types and 54 relationships used to classify the UMLS concepts.
Human Disease Ontology [10] captures and represents knowledge about
human diseases according the four different dimension: disease types, symptoms,
causes and treatments subontologies. Protein Ontology (http://proteinontology.
info/) [11] enables capturing of declarative knowledge about protein domain and
classification of that knowledge to allow reasoning. It enables access not only to
related data but also semi-structured data such as XML or metadata annotations and
unstructured information. A large number of biomedical ontologies is available via
The Open Biomedical Ontologies (http://obofoundry.org/). These ontologies cover
various knowledge domains such as anatomy, biological processes, biochemistry,
health and taxonomy.
In our project we focus on making a channel through which dispersed health information
will be unified under one umbrella to help identify interdependencies
and interrelationship between different aspects of health. Lots of the information is
available but, due to the large body of information, some important information
may escape the users notice and be neglected.
The Web crawling agent and BioAgent system could be used by our system
with some modifications. We can use the same principle of fetching information,
agent migration among multiple locations, information retrieval from each location
and information integration at the end of the trip. But the information we are