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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. AMIA ... Annual Symposium proceedings, 2010. 412 - 416. ISSN 1942-597X

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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: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Lee, Danielle H
Schleyer, Titustitus@pitt.eduTITUS0000-0003-1829-971X
Centers: Other Centers, Institutes, Offices, or Units > Center for Dental Informatics
Date: 2010
Date Type: Publication
Journal or Publication Title: AMIA ... Annual Symposium proceedings
Volume: 2010
Publisher: American Medical Informatics Association
Page Range: 412 - 416
Schools and Programs: School of Dental Medicine > Dental Science
Refereed: Yes
ISSN: 1942-597X
Other ID: NLM PMC3041391
PubMed Central ID: PMC3041391
PubMed ID: 21347011
Date Deposited: 25 Sep 2012 14:26
Last Modified: 25 Aug 2017 05:06


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