Suicide Risk

Suicide risk assessment is a critical area of research aiming to improve early detection and intervention strategies to prevent suicide attempts. Current research heavily utilizes machine learning and deep learning models, including variations of BERT, RoBERTa, and LLMs, to analyze diverse data sources such as social media posts, clinical notes, and speech recordings, often employing multi-task and multi-modal approaches. These advancements offer the potential for more accurate and efficient identification of individuals at risk, facilitating timely interventions and improving the effectiveness of suicide prevention efforts. However, challenges remain regarding data quality, bias mitigation, and ethical considerations surrounding the use of AI in this sensitive domain.