This paper is in the following e-collection/theme issue:

Natural Language Processing (868) Electronic Health Records (1268) Ontologies, Classifications, and Coding (307) Tools, Programs and Algorithms (357)

Published on in Vol 9, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25530, first published .
Similarity-Based Unsupervised Spelling Correction Using BioWordVec: Development and Usability Study of Bacterial Culture and Antimicrobial Susceptibility Reports

Similarity-Based Unsupervised Spelling Correction Using BioWordVec: Development and Usability Study of Bacterial Culture and Antimicrobial Susceptibility Reports

Similarity-Based Unsupervised Spelling Correction Using BioWordVec: Development and Usability Study of Bacterial Culture and Antimicrobial Susceptibility Reports

Authors of this article:

Taehyeong Kim1 Author Orcid Image ;   Sung Won Han1 Author Orcid Image ;   Minji Kang1 Author Orcid Image ;   Se Ha Lee2 Author Orcid Image ;   Jong-Ho Kim3 Author Orcid Image ;   Hyung Joon Joo3 Author Orcid Image ;   Jang Wook Sohn4 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Surian D, Wang Y, Coiera E, Magrabi F. Using automated methods to detect safety problems with health information technology: a scoping review. Journal of the American Medical Informatics Association 2023;30(2):382 View

Conference Proceedings

  1. Lefrandt M, Santoso E, Gunawan A, Tedjasulaksana J. 2025 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). Contextual Spelling Corrector for Indonesian Text Preprocessing: A Comparative Analysis of Large Language Models View