MDD-LLM: Towards Accuracy Large Language Models for Major Depressive Disorder Diagnosis
Journal:
arXiv
Published Date:
Apr 28, 2025
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.
