MLM-based typographical error correction of unstructured medical texts for named entity recognition

BACKGROUND: Unstructured text in medical records, such as Electronic Health Records, contain an enormous amount of valuable information for research; however, it is difficult to extract and structure important information because of frequent typographical errors. Therefore, improving the quality of...

Descripción completa

Detalles Bibliográficos
Autores principales: Lee, Eun Byul, Heo, Go Eun, Choi, Chang Min, Song, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9670595/
https://www.ncbi.nlm.nih.gov/pubmed/36384464
http://dx.doi.org/10.1186/s12859-022-05035-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9670595/
https://www.ncbi.nlm.nih.gov/pubmed/36384464
http://dx.doi.org/10.1186/s12859-022-05035-9

Ejemplares similares