Cargando…
Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature
BACKGROUND: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging...
Autores principales: | Knafou, Julien, Haas, Quentin, Borissov, Nikolay, Counotte, Michel, Low, Nicola, Imeri, Hira, Ipekci, Aziz Mert, Buitrago-Garcia, Diana, Heron, Leonie, Amini, Poorya, Teodoro, Douglas |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240481/ https://www.ncbi.nlm.nih.gov/pubmed/37277872 http://dx.doi.org/10.1186/s13643-023-02247-9 |
Ejemplares similares
-
How to update a living systematic review and keep it alive during a pandemic: a practical guide
por: Heron, Leonie, et al.
Publicado: (2023) -
Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: A living systematic review and meta-analysis
por: Buitrago-Garcia, Diana, et al.
Publicado: (2020) -
Deep learning-based risk prediction for interventional clinical trials based on protocol design: A retrospective study
por: Ferdowsi, Sohrab, et al.
Publicado: (2023) -
Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora
por: Naderi, Nona, et al.
Publicado: (2021) -
Outbreaks of publications about emerging infectious diseases: the case of SARS-CoV-2 and Zika virus
por: Ipekci, Aziz Mert, et al.
Publicado: (2021)