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Peer-Reviewed Publication
BMC Emerg Med2026;26(1)June 16, 2026Journal Article

Deep learning-based natural language processing for critical care identification in pediatric emergency department.

Jiyoung Agatha Kim1, Sangyeon Cho2, Minyoung Hwang3, Dongjoon Lee4, Jangyeong Jeon2, Rhoan Lee5, Changhee Lee3, Junyeong Kim6, Woori Bae7
1Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
2Department of Artificial Intelligence, Chung-Ang University, Seoul, Korea.
3Department of Artificial Intelligence, Korea University, Seoul, Korea.
4VUNO, Seoul, Korea.
5LG CNS, Seoul, Korea.
6Department of Artificial Intelligence, Chung-Ang University, Seoul, Korea. junyeongkim@cau.ac.kr.
7Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea. baewool7777@hanmail.net.
PubMed PMC DOI PDF

Abstract

BACKGROUND: The initial assessment of pediatric emergency patients is challenging due to diverse clinical presentations and the limited applicability of existing mortality scoring systems that require detailed laboratory results often unavailable at first encounter. Deep learning (DL)-based Natural Language Processing (NLP) offers the ability to extract complex patterns within textual data attaine…

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