Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features

BACKGROUND: We aimed to develop integrative machine-learning models using quantitative computed tomography (CT) parameters in addition to initial clinical features to predict the respiratory outcomes of coronavirus disease 2019 (COVID-19). METHODS: This was a retrospective study involving 387 patien...

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Detalles Bibliográficos
Autores principales: Kang, Jieun, Kang, Jiyeon, Seo, Woo Jung, Park, So Hee, Kang, Hyung Koo, Park, Hye Kyeong, Hyun, JongHoon, Song, Je Eun, Kwak, Yee Gyung, Kim, Ki Hwan, Kim, Yeon Soo, Lee, Sung-Soon, Koo, Hyeon-Kyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Original Article on Current Status of Diagnosis and Forecast of COVID-19
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10089866/
https://www.ncbi.nlm.nih.gov/pubmed/37065603
http://dx.doi.org/10.21037/jtd-22-1076

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10089866/
https://www.ncbi.nlm.nih.gov/pubmed/37065603
http://dx.doi.org/10.21037/jtd-22-1076

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