Suicidal Ideation

Suicidal ideation research aims to understand and predict suicidal thoughts, enabling timely intervention and prevention. Current research heavily utilizes machine learning and deep learning, employing models like BERT, RoBERTa, and LLMs to analyze diverse data sources including social media posts, clinical notes, and speech recordings, focusing on identifying patterns and risk factors. These efforts are significant because accurate and efficient detection of suicidal ideation can improve mental health care, potentially reducing suicide rates through early intervention and improved risk assessment. The development of robust and reliable prediction models is a key focus, along with exploring the ethical implications of using AI in this sensitive area.