MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM

TL;DR

MHINDR tackles the challenge of scalable, DSM-5-aligned mental health assessment from user-generated text by integrating an LLM with structured DSM-5 criteria. The framework extracts both temporal and non-temporal features from Reddit data, aggregates per-user signals, and produces DSM-5-based diagnoses along with personalized therapy and behavior-change recommendations. Through an end-to-end pipeline spanning data filtering, feature extraction, user profiling, diagnosis, and guidance, MHINDR demonstrates the feasibility of data-driven, personalized mental health support from public forums. Future work will focus on clinical validation, adaptation to other psychiatric guidelines, and real-time deployment to enhance early detection and timely interventions.

Abstract

Mental health forums offer valuable insights into psychological issues, stressors, and potential solutions. We propose MHINDR, a large language model (LLM) based framework integrated with DSM-5 criteria to analyze user-generated text, dignose mental health conditions, and generate personalized interventions and insights for mental health practitioners. Our approach emphasizes on the extraction of temporal information for accurate diagnosis and symptom progression tracking, together with psychological features to create comprehensive mental health summaries of users. The framework delivers scalable, customizable, and data-driven therapeutic recommendations, adaptable to diverse clinical contexts, patient needs, and workplace well-being programs.

Figures (1)

• Figure 1: MHINDR -- an end-to-end framework for generating user summary and recommendation from text input data