Abstract
BackgroundA systematic review can be defined as a summary of the evidence found in the literature via a systematic search in the available scientific databases. One of the steps involved is article selection, which is typically a laborious task. Machine learning and artificial intelligence can be important tools in automating this step, thus aiding researchers.ObjectiveThe aim of this study is to create models based on an artificial neural network system to automate the article selection process in systematic reviews related to “Mindfulness and Health Promotion.”MethodsThe study will be performed using Python programming software. The system will consist of six main steps: (1) data import, (2) exclusion of duplicates, (3) exclusion of non-articles, (4) article reading and model creation using artificial neural network, (5) comparison of the models, and (6) system sharing. We will choose the 10 most relevant systematic reviews published in the fields of “Mindfulness and Health Promotion” and “Orthopedics” (control group) to serve as a test of the effectiveness of the article selection.ResultsData collection will begin in July 2021, with completion scheduled for December 2021, and final publication available in March 2022.ConclusionsAn automated system with a modifiable sensitivity will be created to select scientific articles in systematic review that can be expanded to various fields. We will disseminate our results and models through the “Observatory of Evidence” in public health, an open and online platform that will assist researchers in systematic reviews.International Registered Report Identifier (IRRID)PRR1-10.2196/26448
Highlights
BackgroundA systematic review (SR) [1] can be defined as a summary of the evidence found in the literature
The objective of this study is to develop a semiautomatic, dynamic, and open source computer system which will carry out the selection of scientific articles in SR, in the area of “Mindfulness and Health Promotion,” after deleting duplicate articles and cleaning the data
We estimate that data collection should be completed in December 2021, and the results should be available in March 2022
Summary
A systematic review (SR) [1] can be defined as a summary of the evidence found in the literature. An SR is an extensive systematized survey on a specific subject in which a careful analysis of the evaluated outcomes is performed in an attempt to reach a single conclusion based on all included studies [2]. In addition to the systematized search, specific criteria are used to evaluate the methodological quality of each selected article These measurements are made using scores that vary according to the design of the study in question [8,9,10]. Wallace et al [15] developed Abstrackr based on an active learning system (Active Learning) and used PubMed as a database, unlike our project that will cover other databases, using ANN Another platform, RobotAnalyst, was created by Przybyła et al [16], but it presents a search and use methodology for artificial intelligence different from this study. Generic software may not be sufficiently sensitive to achieve the accuracy of results afforded by using a complex tool
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