Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII
| Author(s) | Chatr-aryamontri, Andrew | |
| Author(s) | Hirschman, Lynette | |
| Author(s) | Ross, Karen E. | |
| Author(s) | Oughtred, Rose | |
| Author(s) | Krallinger, Martin | |
| Author(s) | Dolinski, Kara | |
| Author(s) | Tyers, Mike | |
| Author(s) | Korves, Tonia | |
| Author(s) | Arighi, Cecilia N. | |
| Date Accessioned | 2022-11-11T16:08:41Z | |
| Date Available | 2022-11-11T16:08:41Z | |
| Publication Date | 2022-10-05 | |
| Description | This article was originally published in Database. The version of record is available at: https://doi.org/10.1093/database/baac084 | en_US |
| Abstract | 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. | en_US |
| Sponsor | National Institutes of Health Office of Research Infrastructure Programs (R01OD010929 to M.T. and K.D.); Canadian Institutes of Health Research (FDN-167277 to M.T.); Canada Research Chair in Systems and Synthetic Biology (to M.T.); National Institutes of Health (2U24HG007822-08, 1R35 GM141873-01 to K.E.R. and C.N.A); Spanish Plan for the Advancement of Language Technology and Proyectos I+D+i2020-AI4PROFHEALTH (PID2020-119266RA-I00 to M.K.); MITRE (W56KGU-18-D-0004 to L.H. and T.K.). The views, opinions and/or findings contained in this report are those of the authors and should not be construed as an official government position, policy or decision. | en_US |
| Citation | Andrew Chatr-aryamontri, Lynette Hirschman, Karen E Ross, Rose Oughtred, Martin Krallinger, Kara Dolinski, Mike Tyers, Tonia Korves, Cecilia N Arighi, Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII, Database, Volume 2022, 2022, baac084, https://doi.org/10.1093/database/baac084 | en_US |
| ISSN | 1758-0463 | |
| URL | https://udspace.udel.edu/handle/19716/31586 | |
| Language | en_US | en_US |
| Publisher | Database | en_US |
| Title | Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII | en_US |
| Type | Article | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Overview of the COVID-19 text mining tool interactive.pdf
- Size:
- 4.05 MB
- Format:
- Adobe Portable Document Format
- Description:
- Main article
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 2.22 KB
- Format:
- Item-specific license agreed upon to submission
- Description: