{'fraction_non_alphanumeric': 0.01753453772582359, 'fraction_numerical': 0.0021253985122210413, 'mean_word_length': 5.822463768115942, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
[The short-term prognostication of characteristics of course and optimization of tactics of medical care support of patients with epilepsy].

In clinical practice the identification of the dynamics of course of focal epilepsies on the basis of available clinical and neurophysiological indices (prognostication) is of great importance. The purpose of the study is the short-term prognostication of the course of focal frontal and temporal epilepsy. The materials and methods. The control (42 patients) and clinical (70 patients) groups were examined. The complex clinical physiological examination was carried out using electroencephalography, cognitive evoked potential, cardiac rhythm variability and the Schulte test. The cluster analysis was applied to allocate the observable patients into groups according to the dynamics of seizures frequency. The artificial neural networks technology based on physiological characteristics was applied to classify patients into groups with different course of disease. The results. The spectral characteristics of electroencephalographic signal had the greatest value for short-term prognostication of course of disease in the group of patients with focal frontal epilepsy. In patients with focal temporal epilepsy, the most significant predictors were the characteristics of cognitive evoked potential and characteristics of function of coherence of electroencephalogram. The conclusions. The developed algorithm of prognostication of unfavorable course of focal frontal epilepsy has high sensitivity, but lower specificity. Contrariwise, in case of temporal epilepsy, high specificity of the proposed algorithm is demonstrative, but its sensitivity is lower. It is recommended to apply these algorithms and to accentuate attention on characteristics of potential parameters at organization of diagnostic process in case of focal epilepsy.