Design of Network-Based Fuzzy Knowledge Bases for Medical Decision-Making Support Systems

Abstract

Modern decision making support systems for physi cians of various specialties exert a substantial effect on quality of medical service. These systems provide consul tation in prognostic, diagnostic, prophylactic, and thera peutic activities.Many commercial systems of this class are presently available for specific medical purposes. Such commer cially available systems are based on expert databases that are tunable to user problems [1 5].In this work, design of network based fuzzy knowl edge bases (NBFKB) for medical decision making sup port systems is discussed. Such bases capable of tuning to user problems were developed at the Department of Biomedical Engineering, Kursk State Technological University.Selection of network systems for medical NBFKB is based on the following factors. Diagnostic and therapeu tic processes contain several stages and human health hypotheses. At each stage of diagnostic and therapeutic processes the hypothesis is supported, rejected, or modi fied. Parallel hypotheses can be put forward in case of combined pathology. The tactics of monitoring methods can also be changed. This change can be fairly sharp. If a stage of decision making procedure is represented as a node in the model of a subject area, branched transitions to other nodes are possible. Many researchers use net work models of a subject area to implement such mecha nisms [1,3,6,7].Decision making models are based on various math ematical methods and information knowledge databases. The NBFKB quality depends on model adequacy.Many medical problems (particularly problems of early diagnosis) use different structures of fuzzy knowledge bases, whereas class structure is not clearly determined.Fuzzy logic with decision rules tuned to a training procedure has been used as the main mathematical appa ratus [5,8].This apparatus does not exclude other approaches (discriminant analysis, group argument method, refer ence methods, etc.). The reference methods solve the problem of scale adaptation to decision making.With due regard to variety of decision rules in net work model nodes, a unified multifunctional decision module is suggested.The problems solved using one unified multifunc tional decision module are associated with a technologi cal component of the general solution. This component can include preliminary diagnosis based on epicrisis and medical examination; retrieval of additional information from patient database; stage of diagnosis elucidation with regard to standard examination results; stage of diagnosis elucidation with regard to results of instrumental exami nation, etc.A diagram of the decision module (DM) of a net work information logical model is shown in Fig. 1. The decision module contains program input/output inter faces with given specifications. The input/output inter faces are for input/output of facts, data, decision rules, addresses, control information, and training information.Interface I 1 provides input/output of human health state parameters to the decision module. Interface I 1 also provides input/output ...

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“…Пользуясь рекомендациями [1,7,8,9,10,11,12] Изменения, происходящие в социально-экономической и политической сфере нашего общества, оказали существенное влияние на всю систему российского образования. Очередной этап модернизации системы образования характеризуется активным внедрением федеральных государственных образовательных стандартов на всех ее ступенях.…”

“…С. 9 Все дети, переехавшие в другую страну, будут испытывать стресс. Меньше испытаний выпадет на долю ребёнка, если материально-экономическая обстановка в семье не нарушена, либо социально-религиозная обстановка на новом месте проживания совпадает с предыдущей ситуацией.…”

“…Специалисты решающие задачи классификации в их четкой и нечеткой интерпретации должны учитывать, что наиболее удачные решения (в смысле обеспечения требуемого качества классификации при минимальных затратах) достигаются если выбираемые математические модели соответствуют реальным структурам данных описывающих состояние и поведение элементов биотехнических систем [4,6,7,8,9].…”

“…В ряде источников [5][6][7] указано, что он используется в тех случаях, когда отказы носят постепенный характер и являются следствием направленных физико-химических изменений вследствие износа (старения), а коэффициент вариации 4 , 0 3 , 0    [5,8]. При стабильных условиях и режимах работы в этот период распределение Гаусса хорошо описывает распределение среднего и гамма-процентного ресурса [9], а также наработку машин до первого капитального ремонта [10].…”

Application of AFE Analog Interfaces for Parametric Estimation of Degree of Synchronization of Electrophysiological Processes

“…The indicators of synchronicity degree can be used in medical decision support systems as the informative fea tures in clear and fuzzy decision rules that form the basis for constructing the relevant knowledge bases [1,3,5,7,8]. In fuzzy systems, the derived features that character ize the degree of process synchronization can be conve niently used as base variables of respective membership functions, which are aggregated in the decision rules with various informative features [4, 6 8].…”