BRI-MH: Behavioral Risk Index for Mental Health — An Interpretable Multimodal LLM-Augmented Framework (Student Abstract)

Authors

  • Mahi Mann Indraprastha Institute of Information Technology Delhi (IIIT-Delhi)
  • Avinash Anand Indraprastha Institute of Information Technology, Delhi (IIIT Delhi), India
  • Rajiv Ratn Shah Department of Computer Science and Engineering, IIIT Delhi, India

DOI:

https://doi.org/10.1609/aaai.v40i48.42252

Abstract

Mental health monitoring faces challenges from fragmented data and opaque risk scores. We present BRI-MH, an in- terpretable multimodal framework combining behavioral sig- nals with cognitive features from large language models to produce a weekly Behavioral Risk Index. Unlike prior work with isolated or black-box scores, BRI-MH offers transpar- ent, actionable insights and links continuous monitoring to adaptive feedback and therapeutic support, bridging digital phenotyping and clinical care.
AAAI-26 / IAAI-26 / EAAI-26 Proceedings Cover

Downloads

Published

2026-03-14

How to Cite

Mann, M., Anand, A., & Shah, R. R. (2026). BRI-MH: Behavioral Risk Index for Mental Health — An Interpretable Multimodal LLM-Augmented Framework (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 40(48), 41310–41312. https://doi.org/10.1609/aaai.v40i48.42252

Issue

Vol. 40 No. 48: EAAI-26 AI for Education, Model AI Assignments, AAAI-26 Emerging Trends, Doctoral Consortium, Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Student Abstract and Poster Program