Artificial Intelligence-driven Solution for Prompt Identification of Mental Health Concerns in Adolescents

Globally, adolescents’ mental health is a major problem, and successful intervention and therapy depend heavily on early identification. In this research, we present the creation of an AI-based system to identify mental health problems in teenagers at an early age. In order to find possible symptoms of mental health issues, the system uses machine learning algorithms to examine data from a variety of sources, such as social media postings, text messages, and behavioral patterns. In order to extract mood, linguistic patterns, and semantic clues suggestive of psychological discomfort, the system incorporates natural language processing techniques. It also uses information from wearable technology and smartphone apps to track activity levels and physiological signs, giving parents a complete picture of their adolescent’s health. Feature extraction, sentiment analysis, anomaly detection, and risk assessment algorithms are important parts of the AI system that work together to identify early warning indicators of mental health problems. To increase accuracy and generalization across various demographic groups and cultural situations, the system makes use of cutting-edge neural network designs that have been trained on a variety of datasets. Throughout the system development and deployment phases, ethical issues such as data protection, confidentiality, and informed permission are carefully taken into account. Integration of the AI system with the current healthcare infrastructure enables smooth communication between mental health specialists, caregivers, and the AI system, facilitating prompt interventions and support services.