Leveraging Prompt-Learning for Structured Information Extraction from Crohn's Disease Radiology Reports in a Low-Resource Language

arXiv:2405.01682v1 Announce Type: new
Abstract: Automatic conversion of free-text radiology reports into structured data using Natural Language Processing (NLP) techniques is crucial for analyzing diseases on a large scale. While effective for tasks in widely spoken languages like English, generative large language models (LLMs) typically underperform with less common languages and can pose potential risks to patient privacy. Fine-tuning local NLP models is hindered by the skewed nature of real-world medical datasets, where rare findings represent a significant data imbalance. …

abstract arxiv conversion cs.ai cs.cl data disease diseases english extraction free generative information information extraction language language models language processing languages large language large language models llms low natural natural language natural language processing nlp processing prompt radiology reports scale spoken structured data tasks text type while