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Authors
Cho, Kyung-Jae http://orcid.org/0000-0003-3564-3287
Kim, Jung Soo http://orcid.org/0000-0001-6603-6768
Lee, Dong Hyun
Lee, Sang‑Min
Song, Myung Jin
Lim, Sung Yoon
Cho, Young-Jae http://orcid.org/0000-0001-6943-4462
Jo, You Hwan http://orcid.org/0000-0002-9507-7603
Shin, Yunseob http://orcid.org/0000-0002-1955-1908
Lee, Yeon Joo http://orcid.org/0000-0001-7697-4272
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Clinical Trials BETA
Clinical trials referenced in this document:
nct04951973 at ClinicalTrials.govDocuments that mention this clinical trial
Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards
https://doi.org/10.1186/s13054-023-04609-0
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Funding
Funding for this research was provided by:
Korea Medical Device Development Fund (202015X02)
Seoul National University Bundang Hospital, South Korea (14-2017-0021)
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More Information
Article History
Received: 22 May 2023
Accepted: 10 August 2023
First Online: 5 September 2023
Declarations
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: This study was strictly observational and conducted based on anonymity. The Ethics Committee and Institutional Review Board of each hospital approved the study protocol as minimal-risk research using data collected for routine clinical practice, and they waived the requirement of informed consent from the participants.
: Not applicable.
: All authors have disclosed that they have no potential conflicts interest with any companies or organizations.
