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Use of graph theory measures to identify errors in record linkage

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Open access
Authors
Randall, Sean
Boyd, James
Ferrante, Anna
Bauer, J.
Semmens, James
Date
2014
Type
Journal Article

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Randall, S. and Boyd, J. and Ferrante, A. and Bauer, J. and Semmens, J. 2014. Use of graph theory measures to identify errors in record linkage. Computer Methods and Programs in Biomedicine. 115 (2): pp. 55-63.
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Computer Methods and Programs in Biomedicine
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NOTICE: This is the author’s version of a work that was accepted for publication in Computer Methods and Programs in Biomedicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Methods and Programs in Biomedicine, Vol. 115, Issue 2. (2014). doi: 10.1016/j.cmpb.2014.03.008

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Abstract

Ensuring high linkage quality is important in many record linkage applications. Current methods for ensuring quality are manual and resource intensive. This paper seeks to determine the effectiveness of graph theory techniques in identifying record linkage errors. A range of graph theory techniques was applied to two linked datasets, with known truth sets. The ability of graph theory techniques to identify groups containing errors was compared to a widely used threshold setting technique. This methodology shows promise; however, further investigations into graph theory techniques are required. The development of more efficient and effective methods of improving linkage quality will result in higher quality datasets that can be delivered to researchers in shorter timeframes.