Mental Health Screening

Mental health screening research focuses on developing automated methods for early detection of mental health disorders, primarily depression and anxiety, using diverse data sources like social media posts and speech samples. Current approaches leverage machine learning, particularly deep learning models such as neural networks, transformers (e.g., BERT), and recurrent convolutional neural networks, often incorporating multimodal data (text, audio, visual) for improved accuracy. These advancements aim to improve accessibility, efficiency, and cost-effectiveness of mental health assessments, potentially leading to earlier interventions and better treatment outcomes.