Use of graph theory measures to identify errors in record linkage

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.

Source Title

Computer Methods and Programs in Biomedicine

DOI

10.1016/j.cmpb.2014.03.008

ISSN

01692607

Remarks

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

URI

http://hdl.handle.net/20.500.11937/3205

Collection

Curtin Research Publications

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.