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
SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques
Qiming Guo, Jinwen Tang, Wenbo Sun, Haoteng Tang, Yi Shang, Wenlu Wang
On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts
Mustofa Ahmed, Abdul Muntakim, Nawrin Tabassum, Mohammad Asifur Rahim, Faisal Muhammad Shah
Enhancing Infant Crying Detection with Gradient Boosting for Improved Emotional and Mental Health Diagnostics
Kyunghun Lee, Lauren M. Henry, Eleanor Hansen, Elizabeth Tandilashvili, Lauren S. Wakschlag, Elizabeth Norton, Daniel S. Pine, Melissa A. Brotman, Francisco Pereira
On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook
Ana-Maria Bucur, Andreea-Codrina Moldovan, Krutika Parvatikar, Marcos Zampieri, Ashiqur R. KhudaBukhsh, Liviu P. Dinu
Mental Disorders Detection in the Era of Large Language Models
Gleb Kuzmin, Petr Strepetov, Maksim Stankevich, Ivan Smirnov, Artem Shelmanov
MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders
Cheng Li, May Fung, Qingyun Wang, Chi Han, Manling Li, Jindong Wang, Heng Ji