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Background: the delay in language development is characterized by qualitative and quantitative underdevelopment of the vocabulary and the lack of formation of expressive speech. This violation belongs to the mildest speech pathologies, however, there is a high probability of the presence of concomitant mental pathology and the occurrence of adaptation problems at school age. In the etiology of delayed language development, its multifactorial nature has been established. Thus, there is a need to develop a tool that predicts the formation of a delay in speech development in children for the timely implementation of preventive measures.Aim of the study: to develop a tool for predicting speech development delay in children under one year old using artificial intelligence algorithms.Patients and methods: 196 children were examined. The mean age was 26.9 months (SD ± 5.5 months). The sample was divided into two groups: the first included patients with delayed speech development (n = 98), the second included children with normal speech development (n = 98). Speech status was assessed using a questionnaire to determine the speech development of a child aged 18 to 36 months (Language Development Survey). In assessing the risk factors for the occurrence of speech development delay, the “Anamnestic Card of the child” was used. To create a neural network that predicts speech delay in children under one year old, a model was developed and trained using the Keras library for the Python 3.0 programming language.Results: the analysis of the accuracy of the neural network showed a high result — 89% of the cases during the training of the model were identified correctly. At the same time, the sensitivity of the model on the test sample was 100%, and the specificity was 90%.Conclusions: the developed method can be used to create a tool for predicting speech development delay in children up to 3 years of age, which will allow for differentiated therapeutic and preventive measures that contribute to the harmonious development of the child.

Psychoses associated with use of modern synthetic psychoactive substances (PAS) have significant differences in clinical features for making accurate diagnosis. These features play important role in correct diagnosis of psychoses, associated with synthetic cannabis (spice), synthetic stimulants (bath salts), and synthetic GABA-agonists (butyrates) still badly investigated. Theaimof this study was to reveal main symptoms and syndromes of psychoses associated with modern synthetic PAS. Methods: clinical and psychopathological, laboratory, statistical. Results. We examined 154patients with psychoses associated with modern synthetic PAS: 53users of synthetic cannabinoids (spices), 54users of synthetic psychostimulants (cathinones, metcathinones, bath salts), and 47users of synthetic GABA-agonists (butyrolactone). Conclusion. Differences in psychotic symptoms in different groups are described.