Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error

BACKGROUND: Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm comparable to human screening that can reduce huma...

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Detalles Bibliográficos
Autores principales: Bannach-Brown, Alexandra, Przybyła, Piotr, Thomas, James, Rice, Andrew S. C., Ananiadou, Sophia, Liao, Jing, Macleod, Malcolm Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Methodology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6334440/
https://www.ncbi.nlm.nih.gov/pubmed/30646959
http://dx.doi.org/10.1186/s13643-019-0942-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6334440/
https://www.ncbi.nlm.nih.gov/pubmed/30646959
http://dx.doi.org/10.1186/s13643-019-0942-7

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