ANALYSIS OF ARTIFICIAL INTELLIGENCE METHODS APPLIED TO SOLVING PSYCHIATRY PROBLEMS
Abstract
The use of artificial intelligence methods in the field of medicine has become widespread,
helping to diagnose, analyze and make recommendations for treatment. Psychiatry is a branch of
medicine that studies mental disorders, methods for their diagnosis and treatment. Her range of
tasks includes not only diagnosis and treatment, but also observation, monitoring and subsequent
rehabilitation of patients. This subject area has significant problems, such as objectivity, inconsistency
in the diagnosis, the complexity of the classification of diseases, and the unpredictability
of the course of the disease. With a number of these problems, the use of machine learning methods
and artificial intelligence algorithms helps to cope. This paper is devoted to a review of research
on artificial intelligence methods used to solve problems in the field of psychiatry.
The relevance of the topic is due to the high need for improvements in this subject area. Specific
issues are presented in this article. Among them, the main directions were identified: data deidentification,
classification of symptom severity, accuracy of condition prediction. To solve them,
the authors used such methods as latent semantic analysis for natural language processing, classification
methods, convolutional neural networks for prediction, and cognitive modeling. Separately,
the effectiveness of hybrid systems, including the implementation of several machine learning
methods at once, is noted. The aim of the study was to highlight the main directions of development
of research in the scientific community, which demonstrate the successful integration of artificial intelligence into psychiatry, as well as to compare them with each other according to the
obtained estimates of the accuracy of the models. Which, in turn, implies the analysis and analysis
of specific algorithms, their performance for specific tasks
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