Clinical Note Structural Knowledge Improves Word Sense Disambiguation.
AMIA Jt Summits Transl Sci Proc
; 2024: 515-524, 2024.
Article
de En
| MEDLINE
| ID: mdl-38827062
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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Langue:
En
Journal:
AMIA Jt Summits Transl Sci Proc
Année:
2024
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique
Pays de publication:
États-Unis d'Amérique