Clinical guidelines are central to the implementation of clinical decision support systems (CDSSs). Addition or revision of clinical guidelines usually causes the (re-) development of new or existing CDSSs. The separate maintenance of clinical knowledge and their driving systems implies extra system development cost and low knowledge delivery efficiency. We propose, in this paper, an approach to liaise the two activities and support a complete knowledge-driven CDSS architecture. It will accommodate and disseminate new knowledge with minimum efforts required to make relevant changes to the systems, but make use of the new knowledge whenever it becomes available. A Multi-Agent System architecture and a rule-based knowledge repository are put together to realize this goal.

A distributed decision support system involving multiple clinical centres is crucial to the diagnosis of rare diseases. Although sharing of valid diagnosed cases can facilitate later decision making, possibly from geographically different centres, the released information could reveal patient privacy if it is not properly protected. Clinical centres may have to impose their distinct regulations and rules that govern the use of their data externally. The collaboration of centres, therefore, must respect the collective policies and ideally, serve users the most appropriate and useful resources possible in the system according to the past experience. In this way, the system’s value is entrusted and even elevated through continuous collaboration. We present in this paper a link-anonymised data scheme and in addition to that, a security model that together enforce privacy data security and secure resource access for distributed clinical centres. Our illustration of the approach involves a prototype medical decision support system, HealthAgents, for brain tumour diagnosis.

In this paper, we describe the unique security issues involved in healthcare domains. These have been addressed to the needs of the HealthAgents project. In the proposed approach, several levels of security have been provided in accordance with Software Engineering principles, ethical regulations for healthcare data, as well as the security requirements usually raised from the distributed clinical settings. The result is the production of a secure and maintainable Multi-Agent System that enables secure communication, uniform home site authentication, and customised resource access authorisation. A security policy rule scheme has been designed for agent interaction modelling. This separates the functional and non-functional (security) requirements but let security policy constraints integrate into the running of the agents via a unified role notion. Each user/agent can play a function role only when its assigned social rights roles permit the access to resources of various types and geographical locations, as specified in the function role behaviour. The approach is illustrated using a comprehensive secure access case.

We present in this paper an adaptive security model for Multi-agent systems. A security meta-model has been developed in which the traditional role concept has been extended. The new concept incorporates the need of both security management as used by role-based access control (RBAC) and agent functional behaviour in agent-oriented Software Engineering (AOSE). Our approach avoids weaknesses of traditional RBAC approaches and provides a practically usable security model for multi-agent systems (MAS). A unified role interaction model framework has been put forward that incorporates not only functional requirements but also security constraints in MAS. A security policy rule scheme has been used to express security requirements in relation to affective roles. The major contribution of the work is that little redevelopment effort will be required when security is to be engineered into the overall MAS architecture, hence minimising the impact of the security requirements changes to the MAS architecture. We illustrate the approach through its potential application in a clinical trial setting involving a prototype medical decision support system, HealthAgents.

Software systems must be capable of coping with continuous requirements changes and at the same time wisely make use of emerging components and services to remain useful in their environment. In this paper, the adaptive agent model (AAM) approach is proposed. The AAM uses configurable interaction model to drive adaptive agent behaviour. The model captures user requirements and is maintained by experts at a high level of abstraction. The AAM interaction model has been discussed with regard to interaction specification and interaction coordination, in line with a coordination language for the OpenKnowledge project. A major benefit of using the approach is agents can dynamically choose disparate components and services already developed for computation via their interaction with each other at runtime, when a new interaction model has been configured for them towards an emerging business goal. A simple expert seeking scenario has been used to illustrate the approach.

Model-Driven Architecture (MDA) supports the transformation from reusable models to executable software. Business representations, however, cannot be fully and explicitly represented in such models for direct transformation into running systems. Thus, once business needs change, the language abstractions used by MDA (e.g. object constraint language/action semantics), being low level, have to be edited directly. We therefore describe an agent-oriented MDA (AMDA) that uses a set of business models under continuous maintenance by business people, reflecting the current business needs and being associated with adaptive agents that interpret the captured knowledge to behave dynamically. Three contributions of the AMDA approach are identified: (1) to Agent-oriented Software Engineering, a method of building adaptive Multi-Agent Systems; (2) to MDA, a means of abstracting high-level business-oriented models to align executable systems with their requirements at runtime; (3) to distributed systems, the interoperability of disparate components and services via the agent abstraction.