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A new approach and gold standard toward author disambiguation in MEDLINE

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Vishnyakova Dina, Rodriguez-Esteban Raul, Rinaldi Fabio,
Project MelanoBase
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Original article (peer-reviewed)

Journal Journal of the American Medical Informatics Association (JAMIA)
Publisher BMJ Publishing Group
Volume (Issue) 26(10)
Page(s) 1037 - 1045
Title of proceedings Journal of the American Medical Informatics Association (JAMIA)
DOI 10.1093/jamia/ocz028

Open Access

Type of Open Access Publisher (Gold Open Access)


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. 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. 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. 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.