Machine learning computational tools to assist the performance of systematic reviews : A mapping review
Cierco Jimenez, Ramon (Universitat Autònoma de Barcelona. Departament de Medicina)
Lee, Teresa (International Agency for Research on Cancer)
Rosillo, Nicolás (Hospital Universitario 12 de Octubre (Madrid))
Cordova, Reynalda (University of Vienna)
Cree, Ian A (International Agency for Research on Cancer)
Gonzalez, Angel (Universitat Autònoma de Barcelona. Departament de Medicina)
Indave Ruiz, Blanca Iciar (International Agency for Research on Cancer)

Data:	2022
Resum:	Within evidence-based practice (EBP), systematic reviews (SR) are considered the highest level of evidence in that they summarize the best available research and describe the progress in a determined field. Due its methodology, SR require significant time and resources to be performed; they also require repetitive steps that may introduce biases and human errors. Machine learning (ML) algorithms therefore present a promising alternative and a potential game changer to speed up and automate the SR process. This review aims to map the current availability of computational tools that use ML techniques to assist in the performance of SR, and to support authors in the selection of the right software for the performance of evidence synthesis. The mapping review was based on comprehensive searches in electronic databases and software repositories to obtain relevant literature and records, followed by screening for eligibility based on titles, abstracts, and full text by two reviewers. The data extraction consisted of listing and extracting the name and basic characteristics of the included tools, for example a tool's applicability to the various SR stages, pricing options, open-source availability, and type of software. These tools were classified and graphically represented to facilitate the description of our findings. A total of 9653 studies and 585 records were obtained from the structured searches performed on selected bibliometric databases and software repositories respectively. After screening, a total of 119 descriptions from publications and records allowed us to identify 63 tools that assist the SR process using ML techniques. This review provides a high-quality map of currently available ML software to assist the performance of SR. ML algorithms are arguably one of the best techniques at present for the automation of SR. The most promising tools were easily accessible and included a high number of user-friendly features permitting the automation of SR and other kinds of evidence synthesis reviews. The online version contains supplementary material available at 10. 1186/s12874-022-01805-4.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Automatization ; Evidence-based practice ; Machine learning ; Mapping review ; Software development ; Systematic reviews
Publicat a:	BMC Medical Research Methodology, Vol. 22 (december 2022) , ISSN 1471-2288

DOI: 10.1186/s12874-022-01805-4
PMID: 36522637

14 p, 1.2 MB

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