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EN
Present medicine uses computers in various applications, especially in a field of a diseases level classification and diagnosis. In many cases an automatic conclusion making units are the main goal of the computer systems usage. The software units are developed for the diseases classification or for monitoring of the disease medical treatment. An example application was described in this paper. It concerns a gait abnormalities level analysis that is described by a data records gathered by insoles of Parotec System for Windows (PSW) [17,18]. The PSW software package is used for visualisation of the gait characteristic static and dynamic characteristic features. In the authors' works many additional data components were distinguished. The field of the applications is located within the neurological gait characteristics also the source applications concern orthopaedics [16,18]. Careful analysis of the data provided the developers with new areas the PSW applications [4,11,13]. For conclusion making units the artificial networks theory was implemented [2,4,11,13]. For more effective training of the neural networks specific characteristic measures were introduced [4,5]. They allow controlling the training process more precisely, avoiding mistakes in current records classification.
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Content available A rough decision to gait disturbances classification
EN
The paper concerns the use of diagnostic measures for detection of gait disturbances for neurological pathologies identification. Project of using the PSW (Parotec System for Windows) system for detection of gait's characteristics described by the expert doctor and collected in knowledge base stored using modified Horn's rules with fuzzy linguistic notions was based on several years of experience in implementation and use of the PSW system. Linguistic notions are computed dynamically with use of diagnostic measure distribution of examined population. It makes system more friendly (isolates expert from questions which values of measures describe physiological gait), on the other hand it provides self-tuning of system during acquisition of still growing amount of measurements collected. Condition of correct pathology classification is to obtain proper description of disturbance from an expert and collection of explorations among pathological and physiological population. The best way of tuning the system is to use it for sift research. An implementation of system is currently at the final stage.
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