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MeSH term explosion and author rank improve expert recommendations.

Lee, Danielle H and Schleyer, Titus (2010) MeSH term explosion and author rank improve expert recommendations. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association, 412 - 416. ISBN UNSPECIFIED

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Information overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank.


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Item Type: Book Section
Status: Published
CreatorsEmailPitt UsernameORCID
Lee, Danielle Hhyl12@pitt.eduHYL12
Schleyer, Titustitus@pitt.eduTITUS0000-0003-1829-971X
Date: 2010
Date Type: Publication
Volume: 2010
Publisher: American Medical Informatics Association
Page Range: 412 - 416
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Title of Book: AMIA Annual Symposium Proceedings
Official URL:
Other ID: PMCID: PMC3041391
Date Deposited: 25 Apr 2012 20:00
Last Modified: 31 Jul 2020 18:59


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