The COVID-19 pandemic has caused massive distress around the globe. The dataset on the influence of COVID-19 on children’s mental health is insufficient globally. The way we put ourselves together socially and in our everyday lives has changed dramatically. College students were one group that were greatly impacted, both academically and psychosocially, as they faced unexpected cessation of in-person classes, were compelled to quit their dorms, and saw a loss of social activities. Long-term psychological difficulties in children and young adults will be a significant burden and a severe task post-COVID-19. Psychological symptoms were linked to the experience of numerous stresses, including the danger of academic viewpoint reduction, widespread e-learning adoption, economic concerns, social constraints, and everyday life consequences associated with the COVID-19 pandemic.
The influence of the pandemic's containment measures on adolescent people’s issues have been reviewed in literature and it was determined that the most common psychological factors are anxiety, stress, depression, frustration, fear, and changes in sleep habits. School and college students require a more effective method for overcoming such psychological effects. Therefore, improving social awareness, psychiatric therapy, continuous education, good nutrition, supporting economic necessities, and environmental adaptation are taken as the fundamental requirements to overcome psychological
distress. The growing demand for healthcare coupled with limited resources has created opportunities for digital and technological solutions such as artificial intelligence (AI) to help solve some of the challenges. AI can be used for improvements in clinical outcomes and patient safety, as well as cost reductions, population measurements, and advancements in research. A significant amount of work and attention has been given to implementing AI in the patient-facing environment, however, potential for improvement remains in “back-end” operations and service provision. Although research has been done on possible applications of AI in mental health and patient flow, not many studies identify the specific opportunities of improving patient flow in inpatient mental health units using AI. In fact, AI is one of the IT techniques that can effectively address all these challenges by creating an intelligent knowledge discovery solution and cognitive decision. AI can automatically process structured, unstructured, financial, legal, and real-world data with various techniques, including semantic analysis, natural language processing, machine learning, deep learning, neural computing and knowledge representation, and reasoning. However, integrating AI with knowledge discovery and psychosocial science to meet the requirement of operations is still challenging.
This Research Topic entitled “Analysis of the Mental Health of School and College Students during the Pandemic: Artificial Intelligence Techniques” focuses on exploring artificial intelligence techniques that enable the discussion and evaluation of the applications of AI in various aspects of depression and other psychiatric fields including psychological symptoms of college and school students. We invite all potential scholars to submit their original work that explores AI techniques in the field of mental health and computer science.
Specific topics of interest include (but are not limited to):
- AI-assisted intelligent decision-making model for behavioral and Mental Health Care
- Data processing architecture for the intersection of physical and mental health with AI enabled techniques
- Intelligent knowledge discovery and data analysis for mental health
- Hybrid intelligence model for extracting hidden trends and patterns of mental problems
- Artificial Intelligence and Human Behavior Modeling and Simulation for Mental Health Conditions
- Knowledge discovery and psychological symptoms for a social media platform with artificial intelligence
- Interactive Computerized Mental Health Programs
- Research towards the integration of AI into mental health and knowledge discovery for the corporate world
- Behavioral states and mental health care driven by machine learning techniques
- Big data-driven digital phenotyping of mental health
The COVID-19 pandemic has caused massive distress around the globe. The dataset on the influence of COVID-19 on children’s mental health is insufficient globally. The way we put ourselves together socially and in our everyday lives has changed dramatically. College students were one group that were greatly impacted, both academically and psychosocially, as they faced unexpected cessation of in-person classes, were compelled to quit their dorms, and saw a loss of social activities. Long-term psychological difficulties in children and young adults will be a significant burden and a severe task post-COVID-19. Psychological symptoms were linked to the experience of numerous stresses, including the danger of academic viewpoint reduction, widespread e-learning adoption, economic concerns, social constraints, and everyday life consequences associated with the COVID-19 pandemic.
The influence of the pandemic's containment measures on adolescent people’s issues have been reviewed in literature and it was determined that the most common psychological factors are anxiety, stress, depression, frustration, fear, and changes in sleep habits. School and college students require a more effective method for overcoming such psychological effects. Therefore, improving social awareness, psychiatric therapy, continuous education, good nutrition, supporting economic necessities, and environmental adaptation are taken as the fundamental requirements to overcome psychological
distress. The growing demand for healthcare coupled with limited resources has created opportunities for digital and technological solutions such as artificial intelligence (AI) to help solve some of the challenges. AI can be used for improvements in clinical outcomes and patient safety, as well as cost reductions, population measurements, and advancements in research. A significant amount of work and attention has been given to implementing AI in the patient-facing environment, however, potential for improvement remains in “back-end” operations and service provision. Although research has been done on possible applications of AI in mental health and patient flow, not many studies identify the specific opportunities of improving patient flow in inpatient mental health units using AI. In fact, AI is one of the IT techniques that can effectively address all these challenges by creating an intelligent knowledge discovery solution and cognitive decision. AI can automatically process structured, unstructured, financial, legal, and real-world data with various techniques, including semantic analysis, natural language processing, machine learning, deep learning, neural computing and knowledge representation, and reasoning. However, integrating AI with knowledge discovery and psychosocial science to meet the requirement of operations is still challenging.
This Research Topic entitled “Analysis of the Mental Health of School and College Students during the Pandemic: Artificial Intelligence Techniques” focuses on exploring artificial intelligence techniques that enable the discussion and evaluation of the applications of AI in various aspects of depression and other psychiatric fields including psychological symptoms of college and school students. We invite all potential scholars to submit their original work that explores AI techniques in the field of mental health and computer science.
Specific topics of interest include (but are not limited to):
- AI-assisted intelligent decision-making model for behavioral and Mental Health Care
- Data processing architecture for the intersection of physical and mental health with AI enabled techniques
- Intelligent knowledge discovery and data analysis for mental health
- Hybrid intelligence model for extracting hidden trends and patterns of mental problems
- Artificial Intelligence and Human Behavior Modeling and Simulation for Mental Health Conditions
- Knowledge discovery and psychological symptoms for a social media platform with artificial intelligence
- Interactive Computerized Mental Health Programs
- Research towards the integration of AI into mental health and knowledge discovery for the corporate world
- Behavioral states and mental health care driven by machine learning techniques
- Big data-driven digital phenotyping of mental health
