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Machine learning computational tools to assist the performance of systematic reviews: A mapping review.
Cierco Jimenez, Ramon; Lee, Teresa; Rosillo, Nicolás; Cordova, Reynalda; Cree, Ian A; Gonzalez, Angel; Indave Ruiz, Blanca Iciar.
Afiliação
  • Cierco Jimenez R; International Agency for Research on Cancer (IARC/WHO), Evidence Synthesis and Classification Branch, Lyon, France. ciercor@iarc.who.int.
  • Lee T; Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain. ciercor@iarc.who.int.
  • Rosillo N; International Agency for Research on Cancer (IARC/WHO), Services to Science and Research Branch, Lyon, France.
  • Cordova R; Servicio de Medicina Preventiva, Hospital Universitario 12 de Octubre, Madrid, Spain.
  • Cree IA; International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, Lyon, France.
  • Gonzalez A; Department of Nutritional Sciences, University of Vienna, Vienna, Austria.
  • Indave Ruiz BI; International Agency for Research on Cancer (IARC/WHO), Evidence Synthesis and Classification Branch, Lyon, France.
BMC Med Res Methodol ; 22(1): 322, 2022 12 16.
Article em En | MEDLINE | ID: mdl-36522637
ABSTRACT

BACKGROUND:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article