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Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII
The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534061/ https://www.ncbi.nlm.nih.gov/pubmed/36197453 http://dx.doi.org/10.1093/database/baac084 |
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author | Chatr-aryamontri, Andrew Hirschman, Lynette Ross, Karen E Oughtred, Rose Krallinger, Martin Dolinski, Kara Tyers, Mike Korves, Tonia Arighi, Cecilia N |
author_facet | Chatr-aryamontri, Andrew Hirschman, Lynette Ross, Karen E Oughtred, Rose Krallinger, Martin Dolinski, Kara Tyers, Mike Korves, Tonia Arighi, Cecilia N |
author_sort | Chatr-aryamontri, Andrew |
collection | PubMed |
description | The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have become crucial hubs for the dissemination of scientific results. Natural language processing (NLP) systems have been recently developed to extract and organize COVID-19 data in reasoning systems. Given this scenario, the BioCreative COVID-19 text mining tool interactive demonstration track was created to assess the landscape of the available tools and to gauge user interest, thereby providing a two-way communication channel between NLP system developers and potential end users. The goal was to inform system designers about the performance and usability of their products and to suggest new additional features. Considering the exploratory nature of this track, the call for participation solicited teams to apply for the track, based on their system’s ability to perform COVID-19-related tasks and interest in receiving user feedback. We also recruited volunteer users to test systems. Seven teams registered systems for the track, and >30 individuals volunteered as test users; these volunteer users covered a broad range of specialties, including bench scientists, bioinformaticians and biocurators. The users, who had the option to participate anonymously, were provided with written and video documentation to familiarize themselves with the NLP tools and completed a survey to record their evaluation. Additional feedback was also provided by NLP system developers. The track was well received as shown by the overall positive feedback from the participating teams and the users. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-4/ |
format | Online Article Text |
id | pubmed-9534061 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-95340612022-10-06 Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII Chatr-aryamontri, Andrew Hirschman, Lynette Ross, Karen E Oughtred, Rose Krallinger, Martin Dolinski, Kara Tyers, Mike Korves, Tonia Arighi, Cecilia N Database (Oxford) Original Article The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have become crucial hubs for the dissemination of scientific results. Natural language processing (NLP) systems have been recently developed to extract and organize COVID-19 data in reasoning systems. Given this scenario, the BioCreative COVID-19 text mining tool interactive demonstration track was created to assess the landscape of the available tools and to gauge user interest, thereby providing a two-way communication channel between NLP system developers and potential end users. The goal was to inform system designers about the performance and usability of their products and to suggest new additional features. Considering the exploratory nature of this track, the call for participation solicited teams to apply for the track, based on their system’s ability to perform COVID-19-related tasks and interest in receiving user feedback. We also recruited volunteer users to test systems. Seven teams registered systems for the track, and >30 individuals volunteered as test users; these volunteer users covered a broad range of specialties, including bench scientists, bioinformaticians and biocurators. The users, who had the option to participate anonymously, were provided with written and video documentation to familiarize themselves with the NLP tools and completed a survey to record their evaluation. Additional feedback was also provided by NLP system developers. The track was well received as shown by the overall positive feedback from the participating teams and the users. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-4/ Oxford University Press 2022-10-05 /pmc/articles/PMC9534061/ /pubmed/36197453 http://dx.doi.org/10.1093/database/baac084 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Article Chatr-aryamontri, Andrew Hirschman, Lynette Ross, Karen E Oughtred, Rose Krallinger, Martin Dolinski, Kara Tyers, Mike Korves, Tonia Arighi, Cecilia N Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII |
title | Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII |
title_full | Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII |
title_fullStr | Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII |
title_full_unstemmed | Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII |
title_short | Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII |
title_sort | overview of the covid-19 text mining tool interactive demonstration track in biocreative vii |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534061/ https://www.ncbi.nlm.nih.gov/pubmed/36197453 http://dx.doi.org/10.1093/database/baac084 |
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