SapBERT-Based Medical Concept Normalization Using SNOMED CT.
Stud Health Technol Inform
; 302: 825-826, 2023 May 18.
Article
en En
| MEDLINE
| ID: mdl-37203507
ABSTRACT
Word vector representations, known as embeddings, are commonly used for natural language processing. Particularly, contextualized representations have been very successful recently. In this work, we analyze the impact of contextualized and non-contextualized embeddings for medical concept normalization, mapping clinical terms via a k-NN approach to SNOMED CT. The non-contextualized concept mapping resulted in a much better performance (F1-score = 0.853) than the contextualized representation (F1-score = 0.322).
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Lenguaje Natural
/
Systematized Nomenclature of Medicine
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2023
Tipo del documento:
Article
País de afiliación:
Austria