Cloud Computing with Intelligent Agents to Support Service Oriented System Control and Management

the past few years, Cloud computing has becoming one of the revolutionary technologies in ICT which grows in both popularity and importance, both in industry and in academic domain. More and more private companies, government organizations and institutions are convinced and happy to promote Cloud to improve both connectivity and instant social ability. For IT services and solutions for business, Cloud-based platform promises to offer better business intelligence and productive experience by using unified communications, consistent collaborated data and service management. It is well believed that Cloud Computing will also bring a revolution in the healthcare IT sector along with other ICT business. To exploit Cloud computing productivity potential, this paper focuses on adopting Cloud computing technologies with agent-based solutions to support service oriented system control and management. The on-going research and practice demonstrates an application to the management of community care provision, which shows transforming to Software-as-a-Service (Saas) with the combination of a private healthcare cloud and integrated agents can improve business efficiency by providing flexible services scheduling, smarter health care services control and management

Community care system design and development with AUML

An approach to the development of an appropriate agent environment is described in which software researchers collaborate with environment builders to enhance the levels of cooperation and support provided within an integrated agent-oriented community system. Agent-oriented Unified Modelling Language (AUML) is a practical approach to the analysis, design, implementation and management of such a software agent system, whilst providing the power and expressiveness necessary to support the specification, design and organisation of a health care service. This paper describes the background of agent-based health care and the fundamental concepts of Agentoriented UML and outlines how this refreshing approach can be used in the analysis, design, development and organization of agent-based community health care systems. Our approach to building agent-oriented software development solutions emphasizes the importance of AUML as a fundamental initial step in producing agent-based architectures and applications. This approach aims to present an effective schedule and methodology for an agent software development process, by addressing the complex agent environments decomposition, abstraction, organization and software development process activities characteristics, whilst reducing the complexity of the complex agent systems' design and development by using and exploiting AUML's productivity potential

A multi-agent intelligent system for detecting unknown adverse drug reactions through communication and collaboration

Several thousands of drugs are currently available on the U.S. market. A complete understanding of the safe use of drugs is not possible at the time when drug is developed or marketed. At that time, the safety information is only obtained from a few thousand people in a typical pre-marketing clinical trial. Clinical trials are not capable of detecting rare adverse drug reactions (ADRs) because of limitations in sample size and trial duration. Early detection of unknown ADRs could save lives and prevent unnecessary hospitalizations. Current methods largely rely on spontaneous reports which suffer from serious underreporting, latency, and inconsistent reporting. Thus they are not ideal for rapidly identifying rare ADRs. In this dissertation, I developed a team-based multi-agent intelligent system approach for proactively detecting potential ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions). The basic idea is that intelligent agents are capable of collaborating with one another by sharing information and knowledge which will accelerate the process of detecting ADR signal pairs. Each agent is equipped with a fuzzy inference engine, which enables it to find the causal relationship between a drug and a potential ADR (i.e., a signal pair). The fuzzy inference uses detection rules developed by me in this dissertation. The detection rules are based on different factors. I have also developed a methodology to find similar patients in the multi-agents system. The developed methodology uses similarity fuzzy rules in order to find similar patients in each agent\u27s patient database. In this dissertation, I developed a cooperative learning mechanism that was used by the agents in identifying ADR signal pairs and finding similar patients. The basic idea is that the agents are capable of collaborating with one another by sharing their knowledge. The agents start collaboration by providing their knowledge (i.e. rules) to the other agents. Using confidence level, the most important and insightful detection rules will be found and used for the benefit of the entire agent system. The new updated rules will lead to improve the agents\u27 decision performance. To evaluate our approach, I designed a four-agent system and implemented it using JADE and FuzzyJess software packages. I choose four because it is representative enough while computing time is still reasonable. To assess the performance of the developed system, I conducted two simulation experiments that involved over 20,000 patients treated at the Veterans Affairs Medical Center in Detroit between 2005 and 2008. From the software standpoint, the four agents collaboratively worked one another as designed. Two physicians on the team independently reviewed the multi-agent system results. The results indicate that the agents can successfully collaborate in finding ADR signal pairs and finding similar patients