Mental Health Assessment

Mental health assessment research focuses on developing accurate and accessible methods for identifying and measuring mental health conditions. Current efforts leverage machine learning, employing various architectures like large language models (LLMs), convolutional neural networks (CNNs), and tree-based classifiers, to analyze diverse data sources including text, speech, facial expressions, and even EEG signals. These advancements aim to improve the efficiency, scalability, and cost-effectiveness of mental health screening, particularly in underserved populations, while also providing valuable insights into the underlying neurobiological and behavioral markers of mental illness. The ultimate goal is to enhance early detection, intervention, and personalized treatment strategies.