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ORIGINAL RESEARCH article
Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 15 - 2024 |
doi: 10.3389/fphys.2024.1389693
This article is part of the Research Topic Artificial Intelligence for Smart Health: Learning, Simulation, and Optimization View all 10 articles
Development of Continuous Warning System for Timely Prediction of Septic Shock
Provisionally accepted- 1 School of Industrial and Management Engineering, Korea University, Seoul, Republic of Korea
- 2 Department of Emergency Medicine, Korea University Anam Hospital, Seoul, Republic of Korea
As delayed treatment of septic shock can lead to an irreversible health state, timely identification of septic shock holds immense value. While numerous approaches have been proposed to build early warning systems, these approaches primarily focus on predicting the future risk of septic shock, irrespective of its precise onset timing. Such early prediction systems without consideration of timeliness fall short in assisting clinicians in taking proactive measures. To address this limitation, we establish a timely warning system for septic shock with data-task engineering, a novel technique regarding the control of data samples and prediction targets.Leveraging machine learning techniques and the real-world electronic medical records from the MIMIC-IV (Medical Information Mart for Intensive Care) database, our system, TEW3S (Timely Early Warning System for Septic Shock), successfully predicted 94% of all shock events with one true alarm for every four false alarms and a maximum lead time of eight hours. This approach emphasizes the often-overlooked importance of prediction timeliness and may provide a practical avenue to develop a timely warning system for acute deterioration in hospital settings, ultimately improving patient outcomes.
Keywords: Early warning system, machine learning, Sepsis, septic shock, artificial intelligence, time-series, Electronic Health Record
Received: 22 Feb 2024; Accepted: 28 Oct 2024.
Copyright: © 2024 Kim, Lee, Kim, Han, Lee, Song and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Juhyun Song, Department of Emergency Medicine, Korea University Anam Hospital, Seoul, Republic of Korea
Hyo Kyung Lee, School of Industrial and Management Engineering, Korea University, Seoul, 136-701, Republic of Korea
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