| Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing and accessing of data, data analysis, and effective use of stored knowledge and data. In this excellent article on "the adolescence of AI in Medicine", Edward H. Shortliffe exposes three factors that may influence the successful integration of AI systems: enhancement of training, international standards, and information infrastructure. Since 1993, information infrastructure has certainly advanced more than the other two factors. In fact, medical informatics has become an integral part of successful medical institution. Many modern hospitals and health care institutions are now well equipped with monitoring and other data collection devices, and data is gathered and shared in inter- and intra-hospital information systems. Modern hospitals are rapidly advancing their information systems. What was before and isolated database or a laboratory information system is now integrated in a larger scale medical information system.The increase in data volume causes difficulties in extracting useful information for decision support. The traditional manual data analysis has become insufficient, and methods for efficient computer-based analysis are indispensable, such as the technologies developed in the area of intelligent data analysis, in particular data abstraction and of data mining. Intelligent data analysis(IDA) encompasses statistical, pattern recognition, machine learning, data abstraction and visualization tools to support the analysis of data and discovery of principles that are encoded within the data. Their role is clearly that of an intelligent assistant that tries to bridge the gap between data gathering and data comprehension, in order to enable the physician to perform his task more efficiently and effectively. The information revolution made it possible to collect and store large volumes of data from diverse sources on electronic media.In these days, the need of combination functions in medicine fields. When the objects to synthesise are numeric values, There are two combination functions (WM,OWA). Both functions, the weighted mean and the OWA operator are to combine values according to a set of weights. However, the meaning of these weights is different in both functions. The weighted mean computes a value that synthesizes the ones of the information sources taking into account the reliability of these sources. The OWA operator, instead, combines the information allowing to weight the values in relation to their ordering position. In order to take advantage of both sets of weights, We propose the WOWA operator that combines the advantages of both combination functions. The WOWA allows the user to weight the reliability of the information source and the values in relation to their relative position.In this article, joint medical and data analysis expertise is brought to bear using fuzzy knowledge representation and "intelligent" aggregation techniques to solve a difficult medical diagnosis problem, that of sleep apnea syndrome screening.Screening of Apnea cases is a difficult diagnosis problem, at present not satisfactorily resolved by standard statistical modeling techniques. We propose that part of the problem is due to the inherent fuzzy nature of a significant part of the data: questionnaire.In this article a fuzzy representation is proposed for the questionnaire.We can consider as derived knowledge the membership grades of the patient responses, and the final aggregated value used for diagnosis of sleep apnea syndrome. The knowledge is represented in the form of reliability and relevance weight for each variable. Two contrasting data aggregation techniques (WOWA,OWA) "fuse" the variables into a diagnosis for each case. We present a way of learning information about the relevance of the data, that is genetic algorithms. comparing this with the definition of the information by the medical expert.If Op is the diagnosis predicted by the aggregation...
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