精神科医生和医疗编码员每天面对大量诊断文本，这项研究展示了如何用LLM自动化ICD编码，大幅减轻行政负担。做医疗NLP或临床信息学的团队值得关注其方法。
该研究利用自然语言处理与机器学习技术，将自由文本的精神科诊断描述自动映射到国际疾病分类（ICD）编码。研究基于14.5万条西班牙语精神科描述数据集，比较了从词袋模型、TF-IDF到大型语言模型（如e5_large、BioLORD、Llama-3-8B）等多种文本表示方法。结果显示，基于Transformer的嵌入方法在捕捉隐含语义和医学术语方面显著优于传统方法，其中e5_large模型通过端到端微调取得了0.866的F1_micro最高分。研究强调，将LLM适配到特定临床术语对于克服“长尾”标签分布和精神科话语的固有歧义至关重要。
Automated ICD Classification of Psychiatric Diagnoses: From Classical NLP to Large Language Models
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.