AI DRIVEN MENTAL HEALTH SURVEILLANCE

Abstract

AI-driven mental health surveillance leverages advanced machine learning and deep learning techniques to monitor, analyze, and predict mental health conditions in individuals. By collecting data from sources such as social media activity, wearable devices, mobile applications, and speech patterns, the system identifies behavioral and emotional changes associated with disorders like depression, anxiety, and stress. Natural Language Processing (NLP) and predictive analytics enable early detection and continuous monitoring, allowing timely intervention and personalized care. This approach reduces reliance on traditional clinical assessments and improves accessibility to mental healthcare services. However, challenges related to data privacy, ethical concerns, and model bias must be addressed to ensure responsible and effective deployment.