Exploring Large Language Models for Detecting Mental Disorders

2024/10/09 by Gleb Kuzmin, Kuzmin, Gleb, Petr Strepetov +7
Computer Science · Neuroscience · Psychology · #Artificial Intelligence (cs.AI) #Brain Tumor Detection and Classification #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning in Healthcare #Mental Health via Writing

paper · pdf · doi:10.48550/arxiv.2410.07129

openalex publication_date 2024/10/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper compares the effectiveness of traditional machine learning methods, encoder-based models, and large language models (LLMs) on the task of detecting depression and anxiety. Five Russian-language datasets were considered, each differing in format and in the method used to define the target pathology class. We tested AutoML models based on linguistic features, several variations of encoder-based Transformers such as BERT, and state-of-the-art LLMs as pathology classification models. The results demonstrated that LLMs outperform traditional methods, particularly on noisy and small datasets where training examples vary significantly in text length and genre. However, psycholinguistic features and encoder-based models can achieve performance comparable to language models when trained on texts from individuals with clinically confirmed depression, highlighting their potential effectiveness in targeted clinical applications.

Related

• Evaluating Large Language Models for Anxiety, Depression, and Stress Detection: Insights into Prompting Strategies and Synthetic Data
• Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts
• Using a fine-tuned large language model for symptom-based depression evaluation
• Leveraging Large Language Models for Cost-Effective, Multilingual Depression Detection and Severity Assessment
• Metareasoning constraints couple narratives, affect and cognition