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Yeha Lee 2 Articles
Pediatrics
Multicenter validation of a deep-learning-based pediatric early-warning system for prediction of deterioration events
Yunseob Shin, Kyung-Jae Cho, Yeha Lee, Yu Hyeon Choi, Jae Hwa Jung, Soo Yeon Kim, Yeo Hyang Kim, Young A Kim, Joongbum Cho, Seong Jong Park, Won Kyoung Jhang
Acute Crit Care. 2022;37(4):654-666.   Published online October 26, 2022
DOI: https://doi.org/10.4266/acc.2022.00976
  • 2,640 View
  • 179 Download
  • 3 Web of Science
  • 5 Crossref
AbstractAbstract PDFSupplementary Material
Background
Early recognition of deterioration events is crucial to improve clinical outcomes. For this purpose, we developed a deep-learning-based pediatric early-warning system (pDEWS) and aimed to validate its clinical performance. Methods: This is a retrospective multicenter cohort study including five tertiary-care academic children’s hospitals. All pediatric patients younger than 19 years admitted to the general ward from January 2019 to December 2019 were included. Using patient electronic medical records, we evaluated the clinical performance of the pDEWS for identifying deterioration events defined as in-hospital cardiac arrest (IHCA) and unexpected general ward-to-pediatric intensive care unit transfer (UIT) within 24 hours before event occurrence. We also compared pDEWS performance to those of the modified pediatric early-warning score (PEWS) and prediction models using logistic regression (LR) and random forest (RF). Results: The study population consisted of 28,758 patients with 34 cases of IHCA and 291 cases of UIT. pDEWS showed better performance for predicting deterioration events with a larger area under the receiver operating characteristic curve, fewer false alarms, a lower mean alarm count per day, and a smaller number of cases needed to examine than the modified PEWS, LR, or RF models regardless of site, event occurrence time, age group, or sex. Conclusions: The pDEWS outperformed modified PEWS, LR, and RF models for early and accurate prediction of deterioration events regardless of clinical situation. This study demonstrated the potential of pDEWS as an efficient screening tool for efferent operation of rapid response teams.

Citations

Citations to this article as recorded by  
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    Intensive Care Medicine – Paediatric and Neonatal.2024;[Epub]     CrossRef
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    Seok Young Song, Won-Kee Choi, Sanggyu Kwak
    Medicine.2023; 102(37): e35057.     CrossRef
  • An advanced pediatric early warning system: a reliable sentinel, not annoying extra work
    Young Joo Han
    Acute and Critical Care.2022; 37(4): 667.     CrossRef
Rapid response system
Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning
Youngnam Lee, Joon-myoung Kwon, Yeha Lee, Hyunho Park, Hugh Cho, Jinsik Park
Acute Crit Care. 2018;33(3):117-120.   Published online August 31, 2018
DOI: https://doi.org/10.4266/acc.2018.00290
  • 12,857 View
  • 532 Download
  • 17 Web of Science
  • 19 Crossref
AbstractAbstract PDF
With the wider adoption of electronic health records, the rapid response team initially believed that mortalities could be significantly reduced but due to low accuracy and false alarms, the healthcare system is currently fraught with many challenges. Rule-based methods (e.g., Modified Early Warning Score) and machine learning (e.g., random forest) were proposed as a solution but not effective. In this article, we introduce the DeepEWS (Deep learning based Early Warning Score), which is based on a novel deep learning algorithm. Relative to the standard of care and current solutions in the marketplace, there is high accuracy, and in the clinical setting even when we consider the number of alarms, the accuracy levels are superior.

Citations

Citations to this article as recorded by  
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  • Artificial intelligence decision points in an emergency department
    Hansol Chang, Won Chul Cha
    Clinical and Experimental Emergency Medicine.2022; 9(3): 165.     CrossRef
  • Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database
    Arom Choi, Min Joung Kim, Ji Min Sung, Sunhee Kim, Jayoung Lee, Heejung Hyun, Hyeon Chang Kim, Ji Hoon Kim, Hyuk-Jae Chang
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    Peng Ran, Kun Dong, Xu Liu, Jing Wang
    SSRN Electronic Journal .2022;[Epub]     CrossRef
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    Bo Young Lee, Sang-Bum Hong
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ACC : Acute and Critical Care