🤖 AI Summary
This study addresses the trade-off among accuracy, standardization, and computational efficiency in NLP for low-resource, non-English medical settings, using unstructured electronic health records from a Polish pediatric rehabilitation hospital. We propose a hybrid approach integrating rule-based systems—offering high precision and low computational cost—with multilingual large language models (LLMs)—providing strong generalization and adaptability. We systematically compare performance on demographic, clinical finding, and medication information extraction tasks using both original Polish text and machine-translated English text. Results show rule-based methods outperform LLMs in age and gender identification, while LLMs significantly improve drug name recognition accuracy. Critically, machine translation introduces non-negligible information loss, degrading downstream performance. This work establishes a new paradigm for resource-constrained, multilingual clinical NLP that balances accuracy, robustness, and practical deployability.
📝 Abstract
Extracting structured medical insights from unstructured clinical text using Natural Language Processing (NLP) remains an open challenge in healthcare, particularly in non-English contexts where resources are scarce. This study presents a comparative analysis of NLP low-compute rule-based methods and Large Language Models (LLMs) for information extraction from electronic health records (EHR) obtained from the Voivodeship Rehabilitation Hospital for Children in Ameryka, Poland. We evaluate both approaches by extracting patient demographics, clinical findings, and prescribed medications while examining the effects of lack of text normalisation and translation-induced information loss. Results demonstrate that rule-based methods provide higher accuracy in information retrieval tasks, particularly for age and sex extraction. However, LLMs offer greater adaptability and scalability, excelling in drug name recognition. The effectiveness of the LLMs was compared with texts originally in Polish and those translated into English, assessing the impact of translation. These findings highlight the trade-offs between accuracy, normalisation, and computational cost when deploying NLP in healthcare settings. We argue for hybrid approaches that combine the precision of rule-based systems with the adaptability of LLMs, offering a practical path toward more reliable and resource-efficient clinical NLP in real-world hospitals.