Fine-Tuning LLMs on Small Medical Datasets: Text Classification and Normalization Effectiveness on Cardiology reports and Discharge records

• URL: http://arxiv.org/abs/2503.21349v1
• Date: Thu, 27 Mar 2025 10:35:56 GMT
• Title: Fine-Tuning LLMs on Small Medical Datasets: Text Classification and Normalization Effectiveness on Cardiology reports and Discharge records
• Authors: Noah Losch, Lucas Plagwitz, Antonius Büscher, Julian Varghese,
• Abstract summary: We investigate the effectiveness of fine-tuning large language models (LLMs) on small medical datasets for text classification and named entity recognition tasks.<n>Our experiments show that fine-tuning improves performance on both tasks, with notable gains observed with as few as 200-300 training examples.
• Score: 0.07499722271664144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the effectiveness of fine-tuning large language models (LLMs) on small medical datasets for text classification and named entity recognition tasks. Using a German cardiology report dataset and the i2b2 Smoking Challenge dataset, we demonstrate that fine-tuning small LLMs locally on limited training data can improve performance achieving comparable results to larger models. Our experiments show that fine-tuning improves performance on both tasks, with notable gains observed with as few as 200-300 training examples. Overall, the study highlights the potential of task-specific fine-tuning of LLMs for automating clinical workflows and efficiently extracting structured data from unstructured medical text.

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