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
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review
Hossein Simchi, Samira Tajik
ALBA: Adaptive Language-based Assessments for Mental Health
Vasudha Varadarajan, Sverker Sikström, Oscar N. E. Kjell, H. Andrew Schwartz
An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives
Young Min Cho, Sunny Rai, Lyle Ungar, João Sedoc, Sharath Chandra Guntuku
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Jaemin Shin, Hyungjun Yoon, Seungjoo Lee, Sungjoon Park, Yunxin Liu, Jinho D. Choi, Sung-Ju Lee
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
Harsh Kumar, Tong Li, Jiakai Shi, Ilya Musabirov, Rachel Kornfield, Jonah Meyerhoff, Ananya Bhattacharjee, Chris Karr, Theresa Nguyen, David Mohr, Anna Rafferty, Sofia Villar, Nina Deliu, Joseph Jay Williams
A Hybrid Approach for Depression Classification: Random Forest-ANN Ensemble on Motor Activity Signals
Anket Patil, Dhairya Shah, Abhishek Shah, Mokshit Gala