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A multi-layer model for the early detection of COVID-19
Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-tempora...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8331231/ https://www.ncbi.nlm.nih.gov/pubmed/34343454 http://dx.doi.org/10.1098/rsif.2021.0284 |
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author | Shmueli, Erez Mansuri, Ronen Porcilan, Matan Amir, Tamar Yosha, Lior Yechezkel, Matan Patalon, Tal Handelman-Gotlib, Sharon Gazit, Sivan Yamin, Dan |
author_facet | Shmueli, Erez Mansuri, Ronen Porcilan, Matan Amir, Tamar Yosha, Lior Yechezkel, Matan Patalon, Tal Handelman-Gotlib, Sharon Gazit, Sivan Yamin, Dan |
author_sort | Shmueli, Erez |
collection | PubMed |
description | Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual and (iv) information reported by the individual during the testing episode. We evaluated our model on 140 682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020. These individuals underwent, in total, 264 516 COVID-19 PCR tests, out of which 16 512 were positive. Our multi-layer model obtained an area under the curve (AUC) of 81.6% when evaluated over all the individuals in the dataset, and an AUC of 72.8% when only individuals who did not report any symptom were included. Furthermore, considering only information collected before the testing episode—i.e. before the individual had the chance to report on any symptom—our model could reach a considerably high AUC of 79.5%. Our ability to predict early on the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be used for a more efficient testing policy. |
format | Online Article Text |
id | pubmed-8331231 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-83312312021-08-11 A multi-layer model for the early detection of COVID-19 Shmueli, Erez Mansuri, Ronen Porcilan, Matan Amir, Tamar Yosha, Lior Yechezkel, Matan Patalon, Tal Handelman-Gotlib, Sharon Gazit, Sivan Yamin, Dan J R Soc Interface Life Sciences–Mathematics interface Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual and (iv) information reported by the individual during the testing episode. We evaluated our model on 140 682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020. These individuals underwent, in total, 264 516 COVID-19 PCR tests, out of which 16 512 were positive. Our multi-layer model obtained an area under the curve (AUC) of 81.6% when evaluated over all the individuals in the dataset, and an AUC of 72.8% when only individuals who did not report any symptom were included. Furthermore, considering only information collected before the testing episode—i.e. before the individual had the chance to report on any symptom—our model could reach a considerably high AUC of 79.5%. Our ability to predict early on the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be used for a more efficient testing policy. The Royal Society 2021-08-04 /pmc/articles/PMC8331231/ /pubmed/34343454 http://dx.doi.org/10.1098/rsif.2021.0284 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Life Sciences–Mathematics interface Shmueli, Erez Mansuri, Ronen Porcilan, Matan Amir, Tamar Yosha, Lior Yechezkel, Matan Patalon, Tal Handelman-Gotlib, Sharon Gazit, Sivan Yamin, Dan A multi-layer model for the early detection of COVID-19 |
title | A multi-layer model for the early detection of COVID-19 |
title_full | A multi-layer model for the early detection of COVID-19 |
title_fullStr | A multi-layer model for the early detection of COVID-19 |
title_full_unstemmed | A multi-layer model for the early detection of COVID-19 |
title_short | A multi-layer model for the early detection of COVID-19 |
title_sort | multi-layer model for the early detection of covid-19 |
topic | Life Sciences–Mathematics interface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8331231/ https://www.ncbi.nlm.nih.gov/pubmed/34343454 http://dx.doi.org/10.1098/rsif.2021.0284 |
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