Application of artificial intelligence technology in depression and suicide management
Conference: CIBDA 2022 - 3rd International Conference on Computer Information and Big Data Applications
03/25/2022 - 03/27/2022 at Wuhan, China
Proceedings: CIBDA 2022
Pages: 4Language: englishTyp: PDF
Authors:
Li, Huinan (Jiangxi University of Traditional Chinese Medicine, Nanchang, China)
Hu, Maorong (Jiangxi University of Traditional Chinese Medicine, Nanchang, China & The First Affiliated Hospital of Nanchang University, China)
Abstract:
Research significance: Depression is a common mental disorder with the characteristics of high morbidity, high disability rate, and high suicide rate. Depression is relatively difficult to diagnose, treat, monitor and manage. In recent years, with the emergence of artificial intelligence technology, it has provided new means for depression and suicide management. Based on the current situation of depression and suicide, this paper discusses the current application results of artificial intelligence in depression diagnosis, depression and suicide monitoring and management, and treatment intervention, so as to provide reference for related research on depression and suicide management based on artificial intelligence technology. Research methods: Search the literature on the application of artificial intelligence technology in depression intervention in recent years through literature navigation. Depression diagnosis, depression monitoring, depression intervention and other aspects were analyzed. The actual technology was compared in the children's motor skills, social interaction, daily life and casual attention. Research conclusion: Artificial intelligence technology is developing rapidly, and it has great advantages over traditional technology in the treatment of depression, but AI still has a lot of room for development, and it also has shortcomings at this stage, because machine learning algorithms are easier to Affected by biases such as racist and sexist rhetoric, key industry players need to ensure the robustness of their AI systems before bringing them to market.