Harnessing machine learning and multivariate analysis to explore global trends in Cannabis sativa research

Abstract This study employs advanced data science techniques to explore global research trends in Cannabis sativa from 1974 to 2024. This research integrated bibliographic datasets from PubMed, Scopus, and Web of Science. By combining latent Dirichlet allocation (LDA) and HJ-Biplot methods, we extra...

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Principais autores:	Javier De La Hoz-M, Karime Montes-Escobar, Carlos Alfredo Salas-Macias
Format:	Artigo
Jezik:	Inglês
Izdano:	BMC 2026-02-01
Serija:	Journal of Cannabis Research
Teme:	Cannabinoids Bibliometric analysis Collaborative networks HJ-Biplot Latent topics
Online dostop:	https://doi.org/10.1186/s42238-026-00397-w
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