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• W4253478903 abstract "<sec> <title>BACKGROUND</title> A 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. </sec> <sec> <title>OBJECTIVE</title> The 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.” </sec> <sec> <title>METHODS</title> The 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. </sec> <sec> <title>RESULTS</title> Data collection will begin in July 2021, with completion scheduled for December 2021, and final publication available in March 2022. </sec> <sec> <title>CONCLUSIONS</title> An 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. </sec> <sec> <title>INTERNATIONAL REGISTERED REPORT</title> PRR1-10.2196/26448 </sec>" @default.
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• W4253478903 date "2020-12-11" @default.
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• W4253478903 title "Automation of Article Selection Process in Systematic Reviews Through Artificial Neural Network Modeling and Machine Learning: Protocol for an Article Selection Model (Preprint)" @default.
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• W4253478903 doi "https://doi.org/10.2196/preprints.26448" @default.
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