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

John Ferguson1, Ailish Hannigan2, Austin Stack3

  • 1Clinical Research Facility, National University of Ireland, Galway, Ireland.

International Journal of Medical Informatics
|December 3, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel record linkage algorithm that improves accuracy with missing data and correlated fields. The computationally efficient method is suitable for linking large databases and is available as the R-package corlink.

Keywords:
Conditional independenceEM-algorithmFellegi/SunterLog-linear modelsRecord linkage

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Area of Science:

  • Data science
  • Statistical computing
  • Bioinformatics

Background:

  • Record linkage is crucial for integrating data from multiple sources.
  • Existing probabilistic linkage models (e.g., Fellegi-Sunter) rely on assumptions of complete data and conditional independence.
  • These assumptions limit performance in real-world datasets with missing information and correlated variables.

Purpose of the Study:

  • To develop a novel record linkage algorithm that overcomes limitations of traditional methods.
  • To improve record linkage accuracy in the presence of missing data and correlated agreement indicators.
  • To provide a computationally efficient solution for large-scale database linkage.

Main Methods:

  • A new probabilistic record linkage algorithm was developed, relaxing assumptions of complete data and conditional independence.
  • The algorithm's performance was evaluated against existing methods.
  • An R-package, corlink, was created to implement the novel algorithm.

Main Results:

  • The proposed algorithm demonstrated improved performance compared to baseline methods, particularly with missing data.
  • The algorithm effectively handles correlation patterns between field agreement indicators.
  • The method is computationally efficient, enabling linkage of millions of record pairs.

Conclusions:

  • The novel record linkage algorithm offers enhanced accuracy and efficiency, especially for datasets with missing or correlated information.
  • The corlink R-package provides accessible implementation of this advanced linkage method.
  • This work advances the field of record linkage for large-scale data integration.