Record Linkage: Current Practice and Future Directions

Lifang Gu, Rohan A. Baxter, Deanne Vickers, Chris P. Rainsford

The Southern medical record · 2003 · 160 citations · 73 references

Concepts

TL;DR

Record linkage, also known as data cleaning or entity reconciliation, rapidly and accurately identifies records for the same entity across data sources, and recent work in information retrieval, federated databases, and data mining proposes alternatives to key components of the standard algorithm. The study evaluates whether proposed alternatives to the standard probabilistic record linkage model improve time, accuracy, and automation for specific applications. The authors present the standard probabilistic record linkage model and algorithm, then assess how the alternatives affect its performance.

Abstract

Record linkage is the task of quickly and accurately identifying records corresponding to the same entity from one or more data sources. Record linkage is also known as data cleaning, entity reconciliation or identification and the merge/purge problem. This paper presents the “standard” probabilistic record linkage model and the associated algorithm. Recent work in information retrieval, federated database systems and data mining have proposed alternatives to key components of the standard algorithm. The impact of these alternatives on the standard approach are assessed. The key question is whether and how these new alternatives are better in terms of time, accuracy and degree of automation for a particular record linkage application.

References

73