A probabilistic model for co-occurrence analysis in bibliometrics.

Publisher: Elsevier Country of Publication: United States NLM ID: 100970413 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-0480 (Electronic) Linking ISSN: 15320464 NLM ISO Abbreviation: J Biomed Inform Subsets: MEDLINE

Publication: Orlando : Elsevier
Original Publication: San Diego, CA : Academic Press, c2001-

Bibliometrics* ; Medical Subject Headings*; Cluster Analysis ; Models, Statistical ; PubMed

The co-occurrence analysis of Medical Subject Heading (MeSH) terms extracted from the PubMed database is popularly used in bibliometrics. Practically for making the result interpretable, it is necessary to apply a certain filter procedure of co-occurrence matrix for removing the low-frequency items due to their low representativeness. Unfortunately, there is rare research referring to determine a critical threshold to remove the noise of co-occurrence matrix. Here, we proposed a probabilistic model for co-occurrence analysis that can provide statistical inferences about whether the paired items co-occur randomly. With help of this model, the dimensionality of co-occurrence matrix could be reduced according to the selected threshold. The conceptual model framework, simulation and practical applications are illustrated in the manuscript. Further details (including all reproducible codes) can be downloaded from the project website: https://github.com/xizhou/co-occurrence-analysis.git.
(Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Keywords: Bibliometrics; Co-occurrence analysis; MeSH term; Probabilistic model; Simulation

Date Created: 20220308 Date Completed: 20220405 Latest Revision: 20220711

20240104

10.1016/j.jbi.2022.104047

35257868