Work

Zhixin: dataset construction and instruction fine-tuning for LLM-based mental disorder diagnosis

Year: 2026

Type: article

Abstract: Automatic diagnosis of mental disorder from chief complaints is an appealing but challenging task. Large Language Models (LLMs) have shown rich knowledge and strong reasoning ability in medical domain. However, mental disorder diagnosis based on chief complaints requires highly specialised knowledge. Lack of corresponding resources hinders LLMs performance on this task. To address this problem, we collect 6,780 complaint-diagnosis pairs spanning 29 different types of mental disorders from a specialized hospital as the dataset. Meanwhile we conduct detailed data cleansing and manual proofreading to protect the privacy. On this basis, we propose the model named \textsc{Zhixin}, a LLM for mental disorder diagnosis fine-tuned on the proposed dataset. The experimental results show that \textsc{Zhixin} outperforms the state-of-the-art approaches in terms of automatic evaluation. It surpasses 3-shot DeepSeek-R1 by 18.54$\%$ on \textit{Overall Accuracy}. Meanwhile, human evaluation results show that the generated response from \textsc{Zhixin} ensures the \textit{Accuracy}, \textit{Safety} and \textit{Professionalism}, which are critical to the diagnosis of mental disorder. In-depth analysis of category-wise performance sheds light on the future research direction, suggesting the focus on relatively rare metal disorders.

Source: Scientific Reports

DOI: 10.1038/s41598-026-77009-3

Authors: Xiujie Zhao, Shutian Liu, Fengdong Sun, Yanrong Dong, Yubo Wang, Changyun Ge

Institutions: Dalian Neusoft University of Information, Fuyang University of Technology

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690