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 co...
| Authors | Fernando Ortega, Raúl Lara-Cabrera, Jorge Dueñas-Lerín et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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