Mental Health
Mental health research increasingly leverages artificial intelligence, particularly large language models (LLMs) and multimodal machine learning, to improve diagnosis, assessment, and treatment. Current efforts focus on developing AI systems capable of analyzing diverse data modalities (text, speech, images) to detect and classify mental health conditions, predict severity, and provide personalized support, often employing techniques like chain-of-thought prompting and knowledge distillation. These advancements hold significant promise for enhancing accessibility, efficiency, and accuracy in mental healthcare, though challenges remain regarding data bias, model interpretability, and ethical considerations.
Papers
Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health
Chandreen Liyanage, Muskan Garg, Vijay Mago, Sunghwan Sohn
Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis
Yeldar Toleubay, Don Joven Agravante, Daiki Kimura, Baihan Lin, Djallel Bouneffouf, Michiaki Tatsubori
LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit Posts
Muskan Garg, Chandni Saxena, Debabrata Samanta, Bonnie J. Dorr
An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts
Muskan Garg, Amirmohammad Shahbandegan, Amrit Chadha, Vijay Mago