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

Natural Language Processing (893) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (213) Machine Learning (2030) Generative Language Models Including ChatGPT (669)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67513, first published .
Predicting Drug–Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach

Predicting Drug–Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach

Predicting Drug–Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach

Authors of this article:

Woohyuk Jeon1 Author Orcid Image ;   Minjae Park1 Author Orcid Image ;   Doyeon An1 Author Orcid Image ;   Wonshik Nam1 Author Orcid Image ;   Ju-Young Shin2 Author Orcid Image ;   Seunghee Lee3 Author Orcid Image ;   Suehyun Lee1 Author Orcid Image
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Woohyuk Jeon   1 * ;   Minjae Park   1 * ;   Doyeon An   1 , MPH ;   Wonshik Nam   1 ;   Ju-Young Shin   2 , PhD ;   Seunghee Lee   3 , PhD ;   Suehyun Lee   1 , PhD

1 Department of Computer Engineering, College of IT Convergence, Gachon University, Seongnam, Republic of Korea

2 School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea

3 Konyang Medical Data Research Group, Konyang University Hospital, Daejeon, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Suehyun Lee, PhD
  • Department of Computer Engineering
  • College of IT Convergence, Gachon University
  • AI·Engineering Building, 317A, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do
  • Seongnam 13120
  • Republic of Korea
  • Phone: 82 010-9012-9364
  • Fax: 82 031-750-5333
  • Email: leesh@gachon.ac.kr