Multi-agent system for early prediction of urinary bladder inflammation disease.
ABSTRACT This paper presents an efficient real-time knowledge base architecture for multi-agent based patient diagnostic system for chronic disease management, basically, the early detection of Inflammation of urinary bladder and Nephritis of renal pelvis origin diseases. The model integrates information stored heterogeneous and geographically distributed healthcare centers. The paper presents two main contributions. First, a proposed multi-agent based system for mining frequent itemsets in distributed databases. Second, the implementation of this model on distributed medical databases in order to generate hidden medical rules. The proposed model can gather information from each department or from different hospitals, and using the cooperative agents it analyzes the data using association rules as a data mining technique. The proposed model improves the diagnostic knowledge and discovers the diseases based on the minimum number of effective tests, thus, providing accurate medical decisions based on cost effective treatments. It can also predict the existence or the absence of the diseases, thus improving the medical service for the patients. The proposed multi-agent system constitute an effort toward the design of intelligent, flexible, and integrated large-scale distributed data mining system.
Conference Paper: Agent Paradigm in Clinical Large-Scale Data Mining Environment.[Show abstract] [Hide abstract]
ABSTRACT: Intelligent agents are new paradigm for developing software applications. More than this, agent-based computing has been hailed as the next significant breakthrough in software development and the new revolution in classification techniques for large-scale data mining environment. Currently, agents are the focus of intense interest on the part of many sub-fields of computer science and artificial intelligence. In our work, we develop multi-agents platform gathering different type of agents. We provide to this platform the ability to operate automatically thanks to autonomous and intelligent agents. This new technology combines the agent approach with the monitoring strategy in order to automatically use the data mining for clinical analysis. Research into data mining in medical diagnosis is important to guide the clinicians in different phases of their diagnostic evaluations. The platform offers an interesting tool for data mining analysis using graph outputs and measures. An implementation of this platform on clinical database is presented. We discuss the importance of our approach and how it supports the data mining and also the possibility to generate and evaluate several association rules according to some scenarios predefined in the intelligent knowledge base of the proposed platform.Proceedings of the 2nd IEEE International Conference on Cognitive Informatics (ICCI 2003), 18-20 August 2003, London, UK; 01/2003
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ABSTRACT: One of significant contributions in medical information technology is the design and implementation of e-health solutions, which provide patients with mobile services to support and optimize their treatment based on the monitoring of certain physiological parameters. This category of e-health is called mobile health monitoring. Although the mobile wireless technologies are now commercially available, there is a need for a computational framework to integrate the technology with the needs of mobile health monitoring. In this paper, we propose a Computer Supported Cooperative Work (CSCW) based technique called awareness modeling to realize the awareness of human roles involved in mobile health monitoring activities using software agents.01/2009; DOI:10.1109/NGMAST.2009.39
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ABSTRACT: In this paper, we present a novel technique of building hybrid decision support systems which integrates traditional decision support systems with agent based models for use in breast cancer analysis for better prediction and recommendation. Our system is based on using queries from data (converted to a standardized electronic template) to provide for simulation variables in an agent-based model. The goal is to develop an ICT tool to assist non-specialist biologist researcher users in performing analysis of large amounts of data by applying simple simulation techniques. To demonstrate the effectiveness of this novel decision support system, an extensive breast cancer data collection exercise was carried out with the support of Hospitals in a previously unexplored region. The collected data was subsequently integrated in an electronic medical record filing system for patients. We also demonstrate the application of agent based modeling and simulation techniques for building simulation models of tumor growth and treatment. Our proposed decision support system also provides a comprehensive query tool which facilitates the use of retrieved data in statistical tools<sup>2</sup> for subsequent interpretation and analysis.Information and Communication Technologies, 2009. ICICT '09. International Conference on; 09/2009