Use of graph theory measures to identify errors in record linkage.
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.
Centre for Data Linkage, Curtin University, Kent Street, Bentley, WA 6102, Australia. Electronic address: sean.randall@curtin.edu.au.
Sean M
SM
Randall
Centre for Data Linkage, Curtin University, Kent Street, Bentley, WA 6102, Australia. Electronic address: J.Boyd@curtin.edu.au.
James H
JH
Boyd
Centre for Data Linkage, Curtin University, Kent Street, Bentley, WA 6102, Australia. Electronic address: A.Ferrante@curtin.edu.au.
Anna M
AM
Ferrante
Centre for Data Linkage, Curtin University, Kent Street, Bentley, WA 6102, Australia. Electronic address: Jacqui.Bauer@curtin.edu.au.
Jacqueline K
JK
Bauer
Centre for Data Linkage, Curtin University, Kent Street, Bentley, WA 6102, Australia. Electronic address: James.Semmens@curtin.edu.au.
James B
JB
Semmens
D016208:Databases, Factual / Q000706:statistics & numerical data; D006801:Humans; D008498:Medical Record Linkage / Q000379:methods* / Q000592:standards; D015233:Models, Statistical; D009517:New South Wales; D012984:Software; D014904:Western Australia
Data quality; Graph theory; Record linkage