Literature DB >> 24768079

Use of graph theory measures to identify errors in record linkage.

Sean M Randall1, James H Boyd2, Anna M Ferrante3, Jacqueline K Bauer4, James B Semmens5.   

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.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Keywords:  Data quality; Graph theory; Record linkage

Mesh:

Year:  2014        PMID: 24768079     DOI: 10.1016/j.cmpb.2014.03.008

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  3 in total

1.  Estimating parameters for probabilistic linkage of privacy-preserved datasets.

Authors:  Adrian P Brown; Sean M Randall; Anna M Ferrante; James B Semmens; James H Boyd
Journal:  BMC Med Res Methodol       Date:  2017-07-10       Impact factor: 4.615

2.  Ensuring Privacy When Integrating Patient-Based Datasets: New Methods and Developments in Record Linkage.

Authors:  Adrian P Brown; Anna M Ferrante; Sean M Randall; James H Boyd; James B Semmens
Journal:  Front Public Health       Date:  2017-03-02

3.  Sociodemographic differences in linkage error: an examination of four large-scale datasets.

Authors:  Sean Randall; Adrian Brown; James Boyd; Rainer Schnell; Christian Borgs; Anna Ferrante
Journal:  BMC Health Serv Res       Date:  2018-09-03       Impact factor: 2.655

  3 in total

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