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Machine learning identifies long COVID patterns from electronic health records
A machine learning algorithm identifies four reproducible clinical subphenotypes of long COVID from the electronic health records of patients with post-acute sequelae of SARS-CoV-2 infection within 30–180 days of infection; these patterns have implications for the treatment and management of long CO...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838487/ https://www.ncbi.nlm.nih.gov/pubmed/36639563 http://dx.doi.org/10.1038/s41591-022-02130-5 |
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collection | PubMed |
description | A machine learning algorithm identifies four reproducible clinical subphenotypes of long COVID from the electronic health records of patients with post-acute sequelae of SARS-CoV-2 infection within 30–180 days of infection; these patterns have implications for the treatment and management of long COVID. |
format | Online Article Text |
id | pubmed-9838487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-98384872023-01-17 Machine learning identifies long COVID patterns from electronic health records Nat Med Research Briefing A machine learning algorithm identifies four reproducible clinical subphenotypes of long COVID from the electronic health records of patients with post-acute sequelae of SARS-CoV-2 infection within 30–180 days of infection; these patterns have implications for the treatment and management of long COVID. Nature Publishing Group US 2023-01-13 2023 /pmc/articles/PMC9838487/ /pubmed/36639563 http://dx.doi.org/10.1038/s41591-022-02130-5 Text en © The Author(s), under exclusive licence to Springer Nature America, Inc. 2023 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Briefing Machine learning identifies long COVID patterns from electronic health records |
title | Machine learning identifies long COVID patterns from electronic health records |
title_full | Machine learning identifies long COVID patterns from electronic health records |
title_fullStr | Machine learning identifies long COVID patterns from electronic health records |
title_full_unstemmed | Machine learning identifies long COVID patterns from electronic health records |
title_short | Machine learning identifies long COVID patterns from electronic health records |
title_sort | machine learning identifies long covid patterns from electronic health records |
topic | Research Briefing |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838487/ https://www.ncbi.nlm.nih.gov/pubmed/36639563 http://dx.doi.org/10.1038/s41591-022-02130-5 |