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PIN64 Identifying COVID-19 Patients from Unstructured Notes: Performance of a Commercial Clinical Named Entity Recognition System
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Published by Elsevier Inc.
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8177587/ http://dx.doi.org/10.1016/j.jval.2021.04.1252 |
| _version_ | 1783703412073824256 |
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| author | Kumar, V. Rasouliyan, L. Long, S. Rao, M.B. |
| author_facet | Kumar, V. Rasouliyan, L. Long, S. Rao, M.B. |
| author_sort | Kumar, V. |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-8177587 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2021 |
| publisher | Published by Elsevier Inc. |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-81775872021-06-05 PIN64 Identifying COVID-19 Patients from Unstructured Notes: Performance of a Commercial Clinical Named Entity Recognition System Kumar, V. Rasouliyan, L. Long, S. Rao, M.B. Value Health Infectious Diseases - Methodological & Statistical Research Published by Elsevier Inc. 2021-06 2021-06-04 /pmc/articles/PMC8177587/ http://dx.doi.org/10.1016/j.jval.2021.04.1252 Text en Copyright © 2021 Published by Elsevier Inc. 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 | Infectious Diseases - Methodological & Statistical Research Kumar, V. Rasouliyan, L. Long, S. Rao, M.B. PIN64 Identifying COVID-19 Patients from Unstructured Notes: Performance of a Commercial Clinical Named Entity Recognition System |
| title | PIN64 Identifying COVID-19 Patients from Unstructured Notes: Performance of a Commercial Clinical Named Entity Recognition System |
| title_full | PIN64 Identifying COVID-19 Patients from Unstructured Notes: Performance of a Commercial Clinical Named Entity Recognition System |
| title_fullStr | PIN64 Identifying COVID-19 Patients from Unstructured Notes: Performance of a Commercial Clinical Named Entity Recognition System |
| title_full_unstemmed | PIN64 Identifying COVID-19 Patients from Unstructured Notes: Performance of a Commercial Clinical Named Entity Recognition System |
| title_short | PIN64 Identifying COVID-19 Patients from Unstructured Notes: Performance of a Commercial Clinical Named Entity Recognition System |
| title_sort | pin64 identifying covid-19 patients from unstructured notes: performance of a commercial clinical named entity recognition system |
| topic | Infectious Diseases - Methodological & Statistical Research |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8177587/ http://dx.doi.org/10.1016/j.jval.2021.04.1252 |
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