用大模型自动把精神科病历转成ICD编码，提升诊断归类效率。
Automated ICD Classification of Psychiatric Diagnoses: From Classical NLP to Large Language Models
- 对比传统NLP与大模型，用Transformer嵌入捕捉医学术语语义
- e5_large模型微调后达到0.866的F1_micro分数，表现最优
- 适合医疗信息自动化、精神科数据标准化的研究者参考
精神健康已成为全球关注重点，临床诊断编码带来巨大行政负担。本研究通过自然语言处理与机器学习技术，实现精神科自由文本描述向国际疾病分类（ICD）的自动化映射。基于包含145,513条西班牙语精神科描述的专用数据集，评估了从传统词频模型（BoW、TF-IDF）到前沿大语言模型（e5_large、BioLORD、Llama-3-8B）等多种文本表示方法。结果表明，基于Transformer的嵌入方法在捕捉隐含语义和复杂医学术语方面显著优于传统方法。其中，e5_large模型经端到端微调后取得最高性能，F1_micro达0.866。研究证明，针对特定临床术语体系适配大模型，是应对长尾标签分布与精神科话语模糊性的关键。
原文摘要 · Abstract (English)
Mental health has become a global priority, leading to a massive administrative burden in the coding of clinical diagnoses. This study proposes the automation of psychiatric diagnostic analysis by mapping free-text descriptions to the International Classification of Diseases (ICD) using Natural Language Processing (NLP) and Machine Learning (ML) techniques. Utilizing a specialized dataset of 145,513 Spanish psychiatric descriptions, various text representation paradigms were evaluated, ranging from classical frequency-based models (BoW, TF-IDF) to state-of-the-art Large Language Models (LLMs) such as e5\_large, BioLORD, and Llama-3-8B. Results indicate that transformer-based embeddings consistently outperform traditional methods by capturing implicit semantic cues and nuanced medical terminology. The e5\_large model, through end-to-end fine-tuning, achieved the highest performance with a $F1_{micro}$ score of 0.866. This research demonstrates that adapting LLMs to specific clinical nomenclature is essential for overcoming the challenges of ``long-tail'' label distributions and the inherent ambiguity of psychiatric discourse.
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