Метод интеллектуального анализа текстовой информации для психиатрической диагностики

Abstract

The automated depression detection system is a progressive technique in terms of improving clinical diagnosis and early medical intervention in cases where depression can have the most serious consequences, including self-harm or suicide. An innovative method of automated detection of depression based on textual data of patients is proposed. The developed method includes modern technologies such as the architecture of the recurrent neural network LSTM and various methods of text vectorization. Experiments conducted on publicly available datasets have confirmed the high efficiency and accuracy of the proposed method compared to the approaches used today. A unique feature of the method is the use of textual characteristics, which ensures the safety of the data provided by patients and eliminates their distortion. This approach not only increases the reliability of the results, but also avoids potential distortion of information in the analysis process. The developed method of automatic assessment of depression has high accuracy and does not require the presence of a doctor, which significantly increases the effectiveness of the process of identifying and assessing the level of depression. This approach can become a promising direction in the development of automated mental health support systems, reducing reaction time and providing more prompt assistance. In the future, the research will include training the model on data in Russian and further tuning of methods, as well as expanding the use of GloVe vectorization to improve contextual understanding of textual data. These steps are aimed at creating a more adapted and effective system for detecting depression in various linguistic contexts.