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Using neural networks to support high-quality evidence mapping
BACKGROUND: The Living Evidence Map Project at the Norwegian Institute of Public Health (NIPH) gives an updated overview of research results and publications. As part of NIPH’s mandate to inform evidence-based infection prevention, control and treatment, a large group of experts are continously moni...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529368/ https://www.ncbi.nlm.nih.gov/pubmed/34674636 http://dx.doi.org/10.1186/s12859-021-04396-x |
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author | Røst, Thomas B. Slaughter, Laura Nytrø, Øystein Muller, Ashley E. Vist, Gunn E. |
author_facet | Røst, Thomas B. Slaughter, Laura Nytrø, Øystein Muller, Ashley E. Vist, Gunn E. |
author_sort | Røst, Thomas B. |
collection | PubMed |
description | BACKGROUND: The Living Evidence Map Project at the Norwegian Institute of Public Health (NIPH) gives an updated overview of research results and publications. As part of NIPH’s mandate to inform evidence-based infection prevention, control and treatment, a large group of experts are continously monitoring, assessing, coding and summarising new COVID-19 publications. Screening tools, coding practice and workflow are incrementally improved, but remain largely manual. RESULTS: This paper describes how deep learning methods have been employed to learn classification and coding from the steadily growing NIPH COVID-19 dashboard data, so as to aid manual classification, screening and preprocessing of the rapidly growing influx of new papers on the subject. Our main objective is to make manual screening scalable through semi-automation, while ensuring high-quality Evidence Map content. CONCLUSIONS: We report early results on classifying publication topic and type from titles and abstracts, showing that even simple neural network architectures and text representations can yield acceptable performance. |
format | Online Article Text |
id | pubmed-8529368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85293682021-10-21 Using neural networks to support high-quality evidence mapping Røst, Thomas B. Slaughter, Laura Nytrø, Øystein Muller, Ashley E. Vist, Gunn E. BMC Bioinformatics Research BACKGROUND: The Living Evidence Map Project at the Norwegian Institute of Public Health (NIPH) gives an updated overview of research results and publications. As part of NIPH’s mandate to inform evidence-based infection prevention, control and treatment, a large group of experts are continously monitoring, assessing, coding and summarising new COVID-19 publications. Screening tools, coding practice and workflow are incrementally improved, but remain largely manual. RESULTS: This paper describes how deep learning methods have been employed to learn classification and coding from the steadily growing NIPH COVID-19 dashboard data, so as to aid manual classification, screening and preprocessing of the rapidly growing influx of new papers on the subject. Our main objective is to make manual screening scalable through semi-automation, while ensuring high-quality Evidence Map content. CONCLUSIONS: We report early results on classifying publication topic and type from titles and abstracts, showing that even simple neural network architectures and text representations can yield acceptable performance. BioMed Central 2021-10-21 /pmc/articles/PMC8529368/ /pubmed/34674636 http://dx.doi.org/10.1186/s12859-021-04396-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Røst, Thomas B. Slaughter, Laura Nytrø, Øystein Muller, Ashley E. Vist, Gunn E. Using neural networks to support high-quality evidence mapping |
title | Using neural networks to support high-quality evidence mapping |
title_full | Using neural networks to support high-quality evidence mapping |
title_fullStr | Using neural networks to support high-quality evidence mapping |
title_full_unstemmed | Using neural networks to support high-quality evidence mapping |
title_short | Using neural networks to support high-quality evidence mapping |
title_sort | using neural networks to support high-quality evidence mapping |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529368/ https://www.ncbi.nlm.nih.gov/pubmed/34674636 http://dx.doi.org/10.1186/s12859-021-04396-x |
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