Artículo

Statistical analysis and machine learning in psychoactive substance use: a bibliometric analysis

Resumen

Because psychoactive substance use is a topic that has received worldwide attention, this area has added several scientific outcomes. It is essential to conduct a comprehensive analysis comprising as many studies as are available to summarize the separate studies and provide an overall view of how the research field has been evolving over the last few decades. This paper performs a bibliometric analysis using a large dataset of published papers from 2000 to 2021. The study examined 1022 publications from those 20 years. About 79% used statistical analyses, and machine learning techniques were utilized by almost 21%. It is worth mentioning that the publications related to statistical analysis were grouped in the following way: multivariate or univariate statistical analysis (52.4%), Bayesian analysis (21.7%), and spatial analysis (50.5%). There were several key points regarding the overall results of the research. Results illustrated that publications had grown significantly during the last two decades. The majority of the publications come from the United States. In addition, the most prolific authors and journals were identified. Over the last decade, due to advanced technological methods, more research has been focused on enhancing and designing Bayesian techniques for using psychoactive substances.
Khamisy-Farah, Rola (55808741800); Gilbey, Peter (55980203000); Furstenau, Leonardo B. (57211463471); Sott, Michele Kremer (57218374403); Farah, Raymond (14013894800); Viviani, Maurizio (57231700200); Bisogni, Maurizio (57232852500); Kong, Jude Dzevela (56305065700); Ciliberti, Rosagemma (6507895664); Bragazzi, Nicola Luigi (57212030091)
Big data for biomedical education with a focus on the covid-19 era: An integrative review of the literature
2021
10.3390/ijerph18178989
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113546517&doi=10.3390%2fijerph18178989&partnerID=40&md5=d9dcc718aa0b917324d63a4b1f8d88c9
Clalit Health Service, Akko, Azrieli Faculty of Medicine, Bar-Ilan University, Safed, 13100, Israel; Azrieli Faculty of medicine, Bar Ilan University, Safed, 13100, Israel; Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre, 90035-190, Brazil; Business School, Unisinos University, Porto Alegre, 91330-002, Brazil; Department of Internal Medicine B, Ziv Medical Center, Azrieli Faculty of Medicine, Bar-Ilan University, Safed, 13100, Israel; TransHumanGene, MedicaSwiss, Cham, Zug, 6330, Switzerland; Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, ON, Canada; Section of History of Medicine and Bioethics, Department of Health Sciences (DISSAL), University of Genoa, Genoa, 16132, Italy
All Open Access; Gold Open Access; Green Open Access
Scopus
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