Automated ICD Classification of Psychiatric Diagnoses: From Classical NLP to Large Language Models

• URL: http://arxiv.org/abs/2605.21154v1
• Date: Wed, 20 May 2026 13:26:05 GMT
• Title: Automated ICD Classification of Psychiatric Diagnoses: From Classical NLP to Large Language Models
• Abstract summary: Mental health has become a global priority, leading to a massive administrative burden in the coding of clinical diagnoses.<n>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.
• Score: 67.51123543731806
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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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