Deep-learning-based automated terminology mapping in OMOP-CDM.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jul 14, 2021

Abstract

OBJECTIVE: Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping.

Authors

  • Byungkon Kang
    Department of Computer Science, State University of New York, Incheon, South Korea.
  • Jisang Yoon
    Graduate School of Information, Yonsei University, Seoul, South Korea.
  • Ha Young Kim
    Department of Financial Engineering, School of Business, Ajou University, Worldcupro 206, Yeongtong-gu, Suwon, 16499, South Korea. Electronic address: hayoungkim@ajou.ac.kr.
  • Sung Jin Jo
    Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, North Gyeongsang,South Korea.
  • Yourim Lee
    RWE Analytics, EvidNet, Seongnam-si, Gyeonggi-do, South Korea.
  • Hye Jin Kam
    Health Innovation Bigdata Center, Asan Institute for Life Sciences, Asan Medical Center, 88, Olympic-ro 43 gil, Songpa-gu, Seoul, 05505, South Korea. Electronic address: kam.hyejin@amc.seoul.kr.

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