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Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons for Amplifying the Signal While Reducing the Noise
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Radiological Society of North America
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995449/ https://www.ncbi.nlm.nih.gov/pubmed/34240059 http://dx.doi.org/10.1148/ryai.2021210011 |
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| author | Driggs, Derek Selby, Ian Roberts, Michael Gkrania-Klotsas, Effrossyni Rudd, James H. F. Yang, Guang Babar, Judith Sala, Evis Schönlieb, Carola-Bibiane |
| author_facet | Driggs, Derek Selby, Ian Roberts, Michael Gkrania-Klotsas, Effrossyni Rudd, James H. F. Yang, Guang Babar, Judith Sala, Evis Schönlieb, Carola-Bibiane |
| author_sort | Driggs, Derek |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-7995449 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2021 |
| publisher | Radiological Society of North America |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-79954492021-03-26 Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons for Amplifying the Signal While Reducing the Noise Driggs, Derek Selby, Ian Roberts, Michael Gkrania-Klotsas, Effrossyni Rudd, James H. F. Yang, Guang Babar, Judith Sala, Evis Schönlieb, Carola-Bibiane Radiol Artif Intell Editorial Radiological Society of North America 2021-03-24 /pmc/articles/PMC7995449/ /pubmed/34240059 http://dx.doi.org/10.1148/ryai.2021210011 Text en 2021 by the Radiological Society of North America, Inc. This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. |
| spellingShingle | Editorial Driggs, Derek Selby, Ian Roberts, Michael Gkrania-Klotsas, Effrossyni Rudd, James H. F. Yang, Guang Babar, Judith Sala, Evis Schönlieb, Carola-Bibiane Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons for Amplifying the Signal While Reducing the Noise |
| title | Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons
for Amplifying the Signal While Reducing the Noise |
| title_full | Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons
for Amplifying the Signal While Reducing the Noise |
| title_fullStr | Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons
for Amplifying the Signal While Reducing the Noise |
| title_full_unstemmed | Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons
for Amplifying the Signal While Reducing the Noise |
| title_short | Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons
for Amplifying the Signal While Reducing the Noise |
| title_sort | machine learning for covid-19 diagnosis and prognostication: lessons
for amplifying the signal while reducing the noise |
| topic | Editorial |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995449/ https://www.ncbi.nlm.nih.gov/pubmed/34240059 http://dx.doi.org/10.1148/ryai.2021210011 |
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