Mapping the knowledge of machine learning in pharmacy: a scientometric analysis in CiteSpace and VOSviewer

Abstract

Background To systematically analyze the knowledge mapping of global development trends and display the status quo, intellectual base and hotspots in ML. Methods We searched for scientific publications related to the application of machine learning (ML) in pharmacy from 1970 to 2021 in the Web of Science Core Collection (WoSCC) on February 22, 2022. CiteSpace and VOSviewer were used for analyzing key features of the application of ML in pharmacy searches, including annual output, countries, organizations, journals, authors, references, research hotspots, and frontiers. Results A total of 13677 studies were extracted as published between 1970 and 2021. Our results suggested that increased numbers of researchers paid more attention to ML applications in pharmacy during this period. Research collaboration was close enough between research countries, organizations and authors. The United States was the country of highest production. California System ranked at the first. Journal of Chemical Information and Modeling published the most studies. Schneider G participated in the highest number of studies. Publication “Breiman L, 2001, Mach Learn, V45, P5” was the one with the highest co-citation number. Research hotspots and frontiers included neural network (NN), artificial neural network (ANN) and deep learning (DL). Conclusion The amount of researches related to ML applications in pharmacy increased from 1990. NN, ANN, and DL were the recent research focuses, therefore more attentions were needed in those research fields.

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