arXiv 2505.000322025-04-28cs.CLcs.AI

MDD-LLM：迈向用于重度抑郁症诊断的高精度大语言模型

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

Macao Polytechnic University（澳门理工大学）；Botou Hospital（泊头医院）；North China University of Science and Technology（华北理工大学）

Yuyang Sha, Hongxin Pan, Wei Xu, Weiyu Meng, Gang Luo, Xinyu Du, Xiaobing Zhai, Henry H. Y. Tong, Caijuan Shi, Kefeng Li

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AI总结：

本文提出MDD-LLM，一个基于微调大语言模型和UK Biobank数据的AI框架，用于重度抑郁症诊断，其70B模型准确率达0.8378，显著优于现有方法。

摘要：

重度抑郁症（MDD）影响着全球超过3亿人，凸显了一个重大的公共卫生问题。然而，医疗资源分布不均和诊断方法的复杂性，导致在许多国家和地区对这种疾病的关注不足。本文介绍了一种名为MDD-LLM的高性能MDD诊断工具，这是一个AI驱动的框架，利用微调过的大语言模型（LLMs）和大量真实世界样本来应对MDD诊断中的挑战。因此，我们从UK Biobank队列中选取了274,348条个人记录，并设计了一种表格数据转换方法，为训练和评估所提出的方法创建了大型语料库。为了展示MDD-LLM的优势，我们进行了全面实验，并在多个评估指标上与现有的基于模型的解决方案进行了多项比较分析。实验结果表明，MDD-LLM（70B）达到了0.8378的准确率和0.8919的AUC（95% CI：0.8799 - 0.9040），显著优于现有的用于MDD诊断的机器学习和深度学习框架。鉴于LLMs在MDD诊断中的探索有限，我们考察了可能影响所提方法性能的众多因素，如表格数据转换技术和不同的微调策略。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。