InterMind: Doctor-Patient-Family Interactive Depression Assessment Empowered by Large Language Models

TL;DR

InterMind is an interactive depression assessment system that leverages large language models to incorporate multi-role inputs, improve interpretability, and assist clinicians with more accurate and efficient diagnoses.

Contribution

The paper introduces InterMind, a novel LLM-powered system that enables multi-role involvement and enhances interpretability in depression assessment, addressing limitations of existing methods.

Findings

Improved diagnostic accuracy validated by clinical assessments.

Enhanced interpretability through retrieval-augmented generation and chain-of-thoughts.

Mitigated hallucination issues in LLMs via data augmentation techniques.

Abstract

Depression poses significant challenges to patients and healthcare organizations, necessitating efficient assessment methods. Existing paradigms typically focus on a patient-doctor way that overlooks multi-role interactions, such as family involvement in the evaluation and caregiving process. Moreover, current automatic depression detection (ADD) methods usually model depression detection as a classification or regression task, lacking interpretability for the decision-making process. To address these issues, we developed InterMind, a doctor-patient-family interactive depression assessment system empowered by large language models (LLMs). Our system enables patients and families to contribute descriptions, generates assistive diagnostic reports for doctors, and provides actionable insights, improving diagnostic precision and efficiency. To enhance LLMs' performance in psychological counseling and diagnostic interpretability, we integrate retrieval-augmented generation (RAG) and chain-of-thoughts (CoT) techniques for data augmentation, which mitigates the hallucination issue of LLMs in specific scenarios after instruction fine-tuning. Quantitative experiments and professional assessments by clinicians validate the effectiveness of our system.