Statistical analysis and machine learning in psychoactive substance use: a bibliometric analysis 2023

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.

Autores	Pacheco, KRP; Correa, CRB
Título	Statistical analysis and machine learning in psychoactive substance use: a bibliometric analysis
Afiliaciones	Universidad del Norte Colombia
Año	2023
DOI	10.5377/nexo.v36i02.16017
Tipo de acceso abierto	gold
Referencia	WOS:001001906400008
Artículo obtenido de:	WOS

Artículo

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

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