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A new computationally efficient algorithm for record linkage with field dependency and missing data imputation

Overview of attention for article published in International Journal of Medical Informatics, January 2018
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Title
A new computationally efficient algorithm for record linkage with field dependency and missing data imputation
Published in
International Journal of Medical Informatics, January 2018
DOI 10.1016/j.ijmedinf.2017.10.021
Pubmed ID
Authors

John Ferguson, Ailish Hannigan, Austin Stack

Abstract

Record linkage algorithms aim to identify pairs of records that correspond to the same individual from two or more datasets. In general, fields that are common to both datasets are compared to determine which record-pairs to link. The classic model for probabilistic linkage was proposed by Fellegi and Sunter and assumes that individual fields common to both datasets are completely observed, and that the field agreement indicators are conditionally independent within the subsets of record pairs corresponding to the same and differing individuals. Herein, we propose a novel record linkage algorithm that is independent of these two baseline assumptions. We demonstrate improved performance of the algorithm in the presence of missing data and correlation patterns between the agreement indicators. The algorithm is computationally efficient and can be used to link large databases consisting of millions of record pairs. An R-package, corlink, has been developed to implement the new algorithm and can be downloaded from the CRAN repository.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 27%
Student > Ph. D. Student 4 18%
Unspecified 2 9%
Student > Bachelor 2 9%
Student > Master 2 9%
Other 3 14%
Unknown 3 14%
Readers by discipline Count As %
Computer Science 7 32%
Medicine and Dentistry 4 18%
Unspecified 2 9%
Nursing and Health Professions 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 1 5%
Unknown 6 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 December 2017.
All research outputs
#10,860,613
of 12,254,300 outputs
Outputs from International Journal of Medical Informatics
#899
of 970 outputs
Outputs of similar age
#283,978
of 342,214 outputs
Outputs of similar age from International Journal of Medical Informatics
#24
of 30 outputs
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