A new approach and gold standard toward author disambiguation in MEDLINE.

Publisher: Oxford University Press Country of Publication: England NLM ID: 9430800 Publication Model: Print Cited Medium: Internet ISSN: 1527-974X (Electronic) Linking ISSN: 10675027 NLM ISO Abbreviation: J Am Med Inform Assoc Subsets: MEDLINE

Publication: 2015- : Oxford : Oxford University Press
Original Publication: Philadelphia, PA : Hanley & Belfus, c1993-

Authorship* ; MEDLINE*/standards ; Machine Learning* ; Reference Standards*; Data Mining/*methods; Algorithms ; Crowdsourcing ; Databases, Bibliographic/standards

Objective: Author-centric analyses of fast-growing biomedical reference databases are challenging due to author ambiguity. This problem has been mainly addressed through author disambiguation using supervised machine-learning algorithms. Such algorithms, however, require adequately designed gold standards that reflect the reference database properly. In this study we used MEDLINE to build the first unbiased gold standard in a reference database and improve over the existing state of the art in author disambiguation.
Materials and Methods: Following a new corpus design method, publication pairs randomly picked from MEDLINE were evaluated by both crowdsourcing and expert curators. Because the latter showed higher accuracy than crowdsourcing, expert curators were tasked to create a full corpus. The corpus was then used to explore new features that could improve state-of-the-art author disambiguation algorithms that would not have been discoverable with previously existing gold standards.
Results: We created a gold standard based on 1900 publication pairs that shows close similarity to MEDLINE in terms of chronological distribution and information completeness. A machine-learning algorithm that includes new features related to the ethnic origin of authors showed significant improvements over the current state of the art and demonstrates the necessity of realistic gold standards to further develop effective author disambiguation algorithms.
Discussion and Conclusion: An unbiased gold standard can give a more accurate picture of the status of author disambiguation research and help in the discovery of new features for machine learning. The principles and methods shown here can be applied to other reference databases beyond MEDLINE. The gold standard and code used for this study are available at the following repository: https://github.com/amorgani/AND/.
(© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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Keywords: MEDLINE; author name disambiguation; gold standard; machine learning; text mining

Date Created: 20190409 Date Completed: 20210201 Latest Revision: 20210201

20231215

PMC7647200

10.1093/jamia/ocz028

30958542