Researchers have developed an automated system to classify psychiatric diagnoses using Natural Language Processing (NLP) and Machine Learning (ML). The study evaluated various text representation methods, including classical models and Large Language Models (LLMs) like e5_large, BioLORD, and Llama-3-8B, on a dataset of over 145,000 Spanish psychiatric descriptions. The findings indicate that transformer-based embeddings significantly outperform traditional methods, with the fine-tuned e5_large model achieving a top F1 score of 0.866. This work highlights the importance of adapting LLMs to specialized clinical language for accurate diagnosis coding. AI
IMPACT Demonstrates LLMs' potential to reduce administrative burden in healthcare by automating complex diagnostic coding.
RANK_REASON The cluster contains an academic paper detailing a new methodology and benchmark results for a specific AI application.
- BioLORD
- e5_large
- International Classification of Diseases
- Llama-3-8B
- Machine Learning
- Natural Language Processing
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