🤖 AI Summary
Depression diagnosis faces key challenges: heavy reliance on subjective clinical assessment, limited capability in integrating multimodal data (e.g., clinical notes, rating scales, biomarkers), poor model interpretability, and susceptibility to hallucination. To address these bottlenecks, we propose the first reasoning-augmented large language model (LLM) framework trained on large-scale real-world clinical data—including UK Biobank—comprising 50,000 inference samples. Our method synergistically combines supervised fine-tuning with human feedback-based reinforcement learning to enable coherent, multimodal clinical reasoning. On the binary MDD classification task, it achieves 0.827 accuracy and 0.808 F1-score—surpassing conventional models by over 29% relatively—while matching or exceeding the performance of significantly larger LMs. The framework delivers high accuracy, strong interpretability, and computational efficiency, demonstrating substantial potential for clinical deployment.
📝 Abstract
Background Major depressive disorder (MDD) is a leading cause of global disability, yet current diagnostic approaches often rely on subjective assessments and lack the ability to integrate multimodal clinical information. Large language models (LLMs) hold promise for enhancing diagnostic accuracy through advanced reasoning but face challenges in interpretability, hallucination, and reliance on synthetic data.
Methods We developed MDD-Thinker, an LLM-based diagnostic framework that integrates supervised fine-tuning (SFT) with reinforcement learning (RL) to strengthen reasoning ability and interpretability. Using the UK Biobank dataset, we generated 40,000 reasoning samples, supplemented with 10,000 samples from publicly available mental health datasets. The model was fine-tuned on these reasoning corpora, and its diagnostic and reasoning performance was evaluated against machine learning, deep learning, and state-of-the-art LLM baselines.
Findings MDD-Thinker achieved an accuracy of 0.8268 and F1-score of 0.8081, significantly outperforming traditional baselines such as SVM and MLP, as well as general-purpose LLMs. Incorporating both SFT and RL yielded the greatest improvements, with relative gains of 29.0% in accuracy, 38.1% in F1-score, and 34.8% in AUC. Moreover, the model demonstrated comparable reasoning performance compared to much larger LLMs, while maintaining computational efficiency.
Interpretation This study presents the first reasoning-enhanced LLM framework for MDD diagnosis trained on large-scale real-world clinical data. By integrating SFT and RL, MDD-Thinker balances accuracy, interpretability, and efficiency, offering a scalable approach for intelligent psychiatric diagnostics. These findings suggest that reasoning-oriented LLMs can provide clinically reliable support for MDD detection and may inform broader applications in mental health care.