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A NOVEL DIAGNOSIS AND SEVERITY PREDICTION ML-BASED MODEL: APPLICATION TO THE IDENTIFICATION AND PREDICTION OF COVID-19 FROM CT RADIOMIC FEATURES
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd.
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9747732/ http://dx.doi.org/10.1016/S1120-1797(22)02293-1 |
| _version_ | 1784849668988993536 |
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| author | Khaniabadi, Pegah Moradi Bouchareb, Yassine Al-Dhuhli, Humoud Shiri, Mr. Isaac Al-Kindi, Faiza Zaidi, Habib Rahmim, Arman |
| author_facet | Khaniabadi, Pegah Moradi Bouchareb, Yassine Al-Dhuhli, Humoud Shiri, Mr. Isaac Al-Kindi, Faiza Zaidi, Habib Rahmim, Arman |
| author_sort | Khaniabadi, Pegah Moradi |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-9747732 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2022 |
| publisher | Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-97477322022-12-14 A NOVEL DIAGNOSIS AND SEVERITY PREDICTION ML-BASED MODEL: APPLICATION TO THE IDENTIFICATION AND PREDICTION OF COVID-19 FROM CT RADIOMIC FEATURES Khaniabadi, Pegah Moradi Bouchareb, Yassine Al-Dhuhli, Humoud Shiri, Mr. Isaac Al-Kindi, Faiza Zaidi, Habib Rahmim, Arman Phys Med Poster Abstracts–Ai Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. 2022-12 2022-12-14 /pmc/articles/PMC9747732/ http://dx.doi.org/10.1016/S1120-1797(22)02293-1 Text en Copyright © 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
| spellingShingle | Poster Abstracts–Ai Khaniabadi, Pegah Moradi Bouchareb, Yassine Al-Dhuhli, Humoud Shiri, Mr. Isaac Al-Kindi, Faiza Zaidi, Habib Rahmim, Arman A NOVEL DIAGNOSIS AND SEVERITY PREDICTION ML-BASED MODEL: APPLICATION TO THE IDENTIFICATION AND PREDICTION OF COVID-19 FROM CT RADIOMIC FEATURES |
| title | A NOVEL DIAGNOSIS AND SEVERITY PREDICTION ML-BASED MODEL: APPLICATION TO THE IDENTIFICATION AND PREDICTION OF COVID-19 FROM CT RADIOMIC FEATURES |
| title_full | A NOVEL DIAGNOSIS AND SEVERITY PREDICTION ML-BASED MODEL: APPLICATION TO THE IDENTIFICATION AND PREDICTION OF COVID-19 FROM CT RADIOMIC FEATURES |
| title_fullStr | A NOVEL DIAGNOSIS AND SEVERITY PREDICTION ML-BASED MODEL: APPLICATION TO THE IDENTIFICATION AND PREDICTION OF COVID-19 FROM CT RADIOMIC FEATURES |
| title_full_unstemmed | A NOVEL DIAGNOSIS AND SEVERITY PREDICTION ML-BASED MODEL: APPLICATION TO THE IDENTIFICATION AND PREDICTION OF COVID-19 FROM CT RADIOMIC FEATURES |
| title_short | A NOVEL DIAGNOSIS AND SEVERITY PREDICTION ML-BASED MODEL: APPLICATION TO THE IDENTIFICATION AND PREDICTION OF COVID-19 FROM CT RADIOMIC FEATURES |
| title_sort | novel diagnosis and severity prediction ml-based model: application to the identification and prediction of covid-19 from ct radiomic features |
| topic | Poster Abstracts–Ai |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9747732/ http://dx.doi.org/10.1016/S1120-1797(22)02293-1 |
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