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Recognition and linking of discontinuous named entities in healthcare: a comparative performance analysis

IntroductionThe recognition and linking of discontinuous named entities (DiscNEs) in healthcare remain challenging due to their fragmented structure and semantic complexity. This study presents a comparative analysis of two state-of-the-art DiscNER models: TriG-NER, a grid-tagging architecture, and...

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Автори: Areej Alhassan, Viktor Schlegel, Rina Carines Cabral, Riza Batista-Navarro, Soyeon Caren Han, Josiah Poon, Goran Nenadic
Формат: Artigo
Мова:Inglês
Опубліковано: Frontiers Media S.A. 2026-06-01
Серія:Frontiers in Digital Health
Предмети:
clinical named entity recognition
discontinuous named entity recognition
healthcare NLP
human phenotype extraction
named entity normalisation
natural language processing
Онлайн доступ:https://www.frontiersin.org/articles/10.3389/fdgth.2026.1758921/full
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