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

Risk prediction requires comprehensive integration of clinical information and concurrent radiological findings. We present an upgraded chest radiograph (CXR) explainable artificial intelligence (xAI) model, which was trained on 241,723 well-annotated CXRs obtained prior to the onset of the COVID-19...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9729627/
https://www.ncbi.nlm.nih.gov/pubmed/36476724
http://dx.doi.org/10.1038/s41598-022-24721-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9729627/
https://www.ncbi.nlm.nih.gov/pubmed/36476724
http://dx.doi.org/10.1038/s41598-022-24721-5

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