Prospective external validation of a deep-learning-based early-warning system for major adverse events in general wards in South Korea

Prospective external validation of a deep-learning-based early-warning system for major adverse events in general wards in South Korea

Background Acute deterioration of patients in general wards often leads to major adverse events (MAEs), including unplanned intensive care unit transfers, cardiac arrest, or death. Traditional early warning scores (EWSs) have shown limited predictive accuracy, with frequent false positives. We condu...

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Main Authors: Taeyong Sim, Eun Young Cho, Ji-hyun Kim, Kyung Hyun Lee, Kwang Joon Kim, Sangchul Hahn, Eun Yeong Ha, Eunkyeong Yun, In-Cheol Kim, Sun Hyo Park, Chi-Heum Cho, Gyeong Im Yu, Byung Eun Ahn, Yeeun Jeong, Joo-Yun Won, Hochan Cho, Ki-Byung Lee
Format: Article
Language:English
Published: Korean Society of Critical Care Medicine 2025-05-01
Series:Acute and Critical Care
Subjects:
adverse events
artificial intelligence
clinical decision support system
deep learning
early warning score
Online Access:http://accjournal.org/upload/pdf/acc-000525.pdf
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http://accjournal.org/upload/pdf/acc-000525.pdf

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