MDD-LLM: Towards Accuracy Large Language Models for Major Depressive Disorder Diagnosis

2025-04-28

AI Summary

Globally, over 300 million individuals suffer from major depressive disorder (MDD), yet disparities in healthcare access and diagnostic complexity hinder timely, accurate identification—necessitating scalable, high-accuracy AI-assisted tools. This study pioneers the systematic application of large language models (LLMs) to clinical MDD risk prediction. Leveraging phenotypic data from 274,000 participants in the UK Biobank, we introduce a structured text encoding paradigm tailored for medical tabular data. A 70-billion-parameter LLM is supervised fine-tuned on this encoded representation and rigorously evaluated using accuracy, area under the ROC curve (AUC), and 95% confidence intervals. The resulting MDD-LLM achieves an accuracy of 0.8378 and an AUC of 0.8919 (95% CI: 0.8799–0.9040), significantly outperforming conventional machine learning and deep learning baselines. This work establishes a novel, empirically validated paradigm for deploying LLMs in precision psychiatry.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: AI for Accessibility

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Major depressive disorder (MDD) impacts more than 300 million people worldwide, highlighting a significant public health issue. However, the uneven distribution of medical resources and the complexity of diagnostic methods have resulted in inadequate attention to this disorder in numerous countries and regions. This paper introduces a high-performance MDD diagnosis tool named MDD-LLM, an AI-driven framework that utilizes fine-tuned large language models (LLMs) and extensive real-world samples to tackle challenges in MDD diagnosis. Therefore, we select 274,348 individual information from the UK Biobank cohort to train and evaluate the proposed method. Specifically, we select 274,348 individual records from the UK Biobank cohort and design a tabular data transformation method to create a large corpus for training and evaluating the proposed approach. To illustrate the advantages of MDD-LLM, we perform comprehensive experiments and provide several comparative analyses against existing model-based solutions across multiple evaluation metrics. Experimental results show that MDD-LLM (70B) achieves an accuracy of 0.8378 and an AUC of 0.8919 (95% CI: 0.8799 - 0.9040), significantly outperforming existing machine learning and deep learning frameworks for MDD diagnosis. Given the limited exploration of LLMs in MDD diagnosis, we examine numerous factors that may influence the performance of our proposed method, such as tabular data transformation techniques and different fine-tuning strategies.

Problem

• Develops MDD-LLM for accurate AI-driven depression diagnosis
• Addresses uneven medical resources and complex diagnostic methods
• Evaluates performance using large real-world dataset (UK Biobank)

Innovation

• Fine-tuned large language models for MDD diagnosis
• Tabular data transformation for training corpus creation
• Comprehensive evaluation against existing diagnostic frameworks