Open AccessAre synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition
Author(s) -
Jianfu Li,
Yujia Zhou,
Xiaoqian Jiang,
Karthik Natarajan,
Serguei Pakhomov,
Hongfang Liu,
Hua Xu
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/ocab112
Subject(s) - computer science , natural language processing , artificial intelligence , named entity recognition , natural language generation , f1 score , natural language , speech recognition , information retrieval , task (project management) , management , economics
: Developing clinical natural language processing systems often requires access to many clinical documents, which are not widely available to the public due to privacy and security concerns. To address this challenge, we propose to develop methods to generate synthetic clinical notes and evaluate their utility in real clinical natural language processing tasks.