<p>Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data</p>

Doyun Kwon; Young Mi Jung; Taekyoung Kim; Kwangsoo Kim; Garam Lee; Dokyoon Kim; Hyung-Chul Lee; Seung-Bo Lee; Seung Mi Lee

doi:10.7490/f1000research.1119257.1

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Kwon D, Jung YM, Kim T et al. Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data [version 1; not peer reviewed]. F1000Research 2022, 11:1418 (poster) (https://doi.org/10.7490/f1000research.1119257.1)

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Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data

Doyun Kwon, Young Mi Jung, Taekyoung Kim, Kwangsoo Kim, Garam Lee, Dokyoon Kim, Hyung-Chul Lee, Seung-Bo Lee¹, Seung Mi Lee²

Published 02 Dec 2022 (https://doi.org/10.7490/f1000research.1119257.1)

Author Affiliations

¹ Keimyung University, South Korea
² Seoul National University, South Korea

Copyright © 2022 Kwon D et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 02 Dec 2022

Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data
[version 1; not peer reviewed]

Doyun Kwon, Young Mi Jung, Taekyoung Kim, Kwangsoo Kim, Garam Lee, Dokyoon Kim, Hyung-Chul Lee, Seung-Bo Lee¹, Seung Mi Lee²

Author Affiliations

¹ Keimyung University, South Korea
² Seoul National University, South Korea

Presented at

Pacific Symposium on Biocomputing (PSB), 2023

Competing Interests

No competing interests were disclosed

Keywords

Deep learning, precision medicine, massive transfusion, intraoperative

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