Author:

Keywords:

Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Information Systems, Computer Science, entity-aware pre-training, named entity recognition, clinical NER tool, contrastive learning, inflammatory bowel disease, language modeling, natural language processing, information extraction, STADIUS-24-114, C3/20/117#56125209, IBOF/23/064#57356938, S005319N#55488662, T001919N#55417627, 11K5623N|11K5625N#56485865, 4007 Control engineering, mechatronics and robotics, 4602 Artificial intelligence, 4611 Machine learning

Abstract:

The digitization of healthcare records has revolutionized medical research and patient care, with electronic health records (EHRs) containing a wealth of structured and unstructured data. Extracting valuable information from unstructured clinical text presents a significant challenge, necessitating automated tools for efficient data mining. Natural language processing (NLP) methods have been pivotal in this endeavor, aiming to extract crucial clinical concepts embedded within free-form text. Our research addresses the imperative for robust biomedical entity extraction, focusing specifically on inflammatory bowel disease (IBD). Leveraging novel domain-specific pre-training and entity-aware masking strategies with contrastive learning, we fine-tune and adapt a general language model to be better adapted to IBD-related information extraction scenarios. Our named entity recognition (NER) tool streamlines the retrieval process, supporting annotation, correction, and visualization functionalities. In summary, we developed a comprehensive pipeline for clinical Dutch NER encompassing an efficient domain adaptation strategy with domain-aware masking and model fine-tuning enhancements, and an end-to-end entity extraction tool, significantly advancing medical record curation and clinical workflows.