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 F1micro 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.
@article{arxiv.2605.21154,
title = {Automated ICD Classification of Psychiatric Diagnoses: From Classical NLP to Large Language Models},
author = {Fernando Ortega and Raúl Lara-Cabrera and Jorge Dueñas-Lerín and Alejandro de la Torre-Luque and Mercé Salvador Robert and Enrique Baca-García},
journal= {arXiv preprint arXiv:2605.21154},
year = {2026}
}