Automated ICD Classification of Psychiatric Diagnoses: From Classical NLP to Large Language Models
arXiv:2605.21154v1 Announce Type: cross 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)
The increasing availability of large language models and specialized datasets, coupled with the global prioritization of mental health, makes this automation timely.
Automating ICD classification in psychiatric diagnoses significantly reduces administrative burdens and improves the efficiency and accuracy of clinical coding, impacting healthcare systems globally.
Clerical tasks associated with psychiatric diagnosis coding can be largely automated, freeing up healthcare professionals for direct patient care and improving data consistency.
- · Healthcare systems
- · Mental health patients
- · AI/NLP developers
- · Medical coders
- · Inefficient manual coding processes
Increased efficiency in mental healthcare administration and data management.
Improved epidemiological tracking and resource allocation for mental health services due to better data quality.
Potential for new AI-driven diagnostic support tools trained on massive, well-classified mental health datasets.
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Read at arXiv cs.LG