ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2024
Clinical Note Structural Knowledge Improves Word Sense Disambiguation.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A neural-symbolic AI agent system for biomedical concept mapping.NPJ digital medicine · 2026Article
- Scalable scientific interest profiling using large language models.Journal of biomedical informatics · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Clinical notes are full of ambiguous medical abbreviations. Contextual knowledge has been leveraged by recent learning-based approaches for sense disambiguation. Previous findings indicated that structural elements of clinical notes entail useful characteristics for informing different interpretations of abbreviations, yet they have remained underutilized and have not been fully investigated. To our best knowledge, the only study exploring note structures simply enumerated the headers in the notes, where such representations are not semantically meaningful. This paper describes a learning-based approach using the note structure represented by the semantic types predefined in Unified Medical Language System (UMLS). We evaluated the representation in addition to the widely used N-gram with three learning models on two different datasets. Experiments indicate that our feature augmentation consistently improved model performance for abbreviation disambiguation, with the optimal F1 score of 0.93.
Identifiers
38827062PMC11141859What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.