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

Standards and Interoperability (126) Ontologies, Classifications, and Coding (318) Development and Evaluation of Research Methods, Instruments and Tools (718) Methods and Instruments in Medical Informatics (185) Natural Language Processing (892) Machine Learning (2027)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71687, first published .
Detecting Redundant Health Survey Questions by Using Language-Agnostic Bidirectional Encoder Representations From Transformers Sentence Embedding: Algorithm Development Study

Detecting Redundant Health Survey Questions by Using Language-Agnostic Bidirectional Encoder Representations From Transformers Sentence Embedding: Algorithm Development Study

Detecting Redundant Health Survey Questions by Using Language-Agnostic Bidirectional Encoder Representations From Transformers Sentence Embedding: Algorithm Development Study

Authors of this article:

Sunghoon Kang1 Author Orcid Image ;   Hyewon Park1 Author Orcid Image ;   Ricky Taira2 Author Orcid Image ;   Hyeoneui Kim1 Author Orcid Image
Article Authors Cited by Tweetations (1) Metrics Author Video

Sunghoon Kang   1 , MS ;   Hyewon Park   1 , BSN, RN ;   Ricky Taira   2 , PhD ;   Hyeoneui Kim   1 , RN, PhD

1 College of Nursing, Seoul National University, Seoul, Republic of Korea

2 The Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States

Corresponding Author:

  • Hyeoneui Kim, RN, PhD
  • College of Nursing
  • Seoul National University
  • 103 Daehak-ro, Jongno-gu
  • Seoul 03080
  • Republic of Korea
  • Phone: 82 027408483
  • Email: ifilgood@snu.ac.kr