Corrigendum to: Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach

Suncheol Heo; Jae Yong Yu; Eun Ae Kang; Hyunah Shin; Kyeongmin Ryu; Chungsoo Kim; Yebin Chega; Hyojung Jung; Suehyun Lee; Rae Woong Park; Kwangsoo Kim; Yul Hwangbo; Jae-Hyun Lee; Yu Rang Park

doi:10.4258/hir.2024.30.2.168

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Heo, Yu, Kang, Shin, Ryu, Kim, Chega, Jung, Lee, Park, Kim, Hwangbo, Lee, and Park: Corrigendum to: Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach

Corrigendum

Healthcare Informatics Research 2024;30(2):168.

Published online: April 30, 2024

DOI: https://doi.org/10.4258/hir.2024.30.2.168

Corrigendum to: Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach

Suncheol Heo, Jae Yong Yu, Eun Ae Kang, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Yebin Chega, Hyojung Jung, Suehyun Lee, Rae Woong Park, Kwangsoo Kim, Yul Hwangbo, Jae-Hyun Lee, Yu Rang Park

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.properly cited.

This corrects the article "Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach" in Volume 29 on page 246.

https://doi.org/10.4258/hir.2023.29.3.246

Healthc Inform Res 2023;29(3):246–55

The authors regret that the Acknowledgments have missing information. The correct funding statement in the ACKNOWLEDGEMENT section is given below:

This study was supported by the Bio-Industrial Technology Development Program (No. 20014841), funded by the Ministry of Trade, Industry & Energy (MOTIE, South Korea). We would like to thank the MCU (Medical informatics Collaborative Unit) members of Yonsei University College of Medicine for their assistance in data analysis. This study was also supported by a grant from the Korea Institute of Drug Safety and Risk Management in 2021.

The authors would like to apologize for any inconvenience caused.

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