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Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach

Crossref DOI link: https://doi.org/10.1038/s41598-022-24721-5

Published Online: 2022-12-07

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Chung, Joowon

Kim, Doyun

Choi, Jongmun

Yune, Sehyo

Song, Kyoung Doo

Kim, Seonkyoung

Chua, Michelle

Succi, Marc D.

Conklin, John

Longo, Maria G. Figueiro

Ackman, Jeanne B.

Petranovic, Milena

Lev, Michael H.

Do, Synho
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Text and Data Mining valid from 2022-12-07

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Article History

Received: 21 April 2022

Accepted: 18 November 2022

First Online: 7 December 2022

Change Date: 15 March 2023

Change Type: Correction

Change Details: A Correction to this paper has been published:

Change Details: https://doi.org/10.1038/s41598-023-31333-0

Competing interests

: The authors declare no competing interests.

Updates are available Correction dated 2023-03-15

Click to view Correction: https://doi.org/10.1038/s41598-023-31333-0