Open AccessDeep-learning-based automated terminology mapping in OMOP-CDM
Author(s) -
Bada Kang,
Jisang Yoon,
Ha Young Kim,
Sung Jin Jo,
Yourim Lee,
Hye Jin Kam
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocab030
Subject(s) - computer science , word embedding , matching (statistics) , artificial intelligence , semantic matching , semantics (computer science) , word (group theory) , terminology , natural language processing , code (set theory) , sample (material) , standardization , similarity (geometry) , semantic similarity , component (thermodynamics) , embedding , data mining , image (mathematics) , programming language , linguistics , statistics , philosophy , chemistry , mathematics , set (abstract data type) , chromatography , operating system , physics , thermodynamics
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.