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Author Correction: 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
| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015513/ https://www.ncbi.nlm.nih.gov/pubmed/36922618 http://dx.doi.org/10.1038/s41598-023-31333-0 |
| _version_ | 1784907219256475648 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Chung, Joowon |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-10015513 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2023 |
| publisher | Nature Publishing Group UK |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-100155132023-03-15 Author Correction: 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 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 Sci Rep Author Correction Nature Publishing Group UK 2023-03-15 /pmc/articles/PMC10015513/ /pubmed/36922618 http://dx.doi.org/10.1038/s41598-023-31333-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
| spellingShingle | Author Correction 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 Author Correction: 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 |
| title | Author Correction: 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 |
| title_full | Author Correction: 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 |
| title_fullStr | Author Correction: 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 |
| title_full_unstemmed | Author Correction: 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 |
| title_short | Author Correction: 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 |
| title_sort | author correction: 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 |
| topic | Author Correction |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015513/ https://www.ncbi.nlm.nih.gov/pubmed/36922618 http://dx.doi.org/10.1038/s41598-023-31333-0 |
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