J Biomed Inform - Improving record linkage performance in the presence of missing linkage data.


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TRODUCTION: Existing record linkage methods do not handle missing linking field values in an efficient and effective manner. The objective of this study is to investigate three novel methods for improving the accuracy and efficiency of record linkage when record linkage fields have missing values.METHODS: By extending the Fellegi-Sunter scoring implementations available in the open-source Fine-grained Record Linkage (FRIL) software system we developed three novel methods to solve the missing data problem in record linkage, which we refer to as: Weight Redistribution, Distance Imputation, and Linkage Expansion. Weight Redistribution removes fields with missing data from the set of quasi-identifiers and redistributes the weight from the missing attribute based on relative proportions across the remaining available linkage fields. Distance Imputation imputes the distance between the missing data fields rather than imputing the missing data value. Linkage Expansion adds previously considered non-linkage fields to the linkage field set to compensate for the missing information in a linkage field. We tested the linkage methods using simulated data sets with varying field value corruption rates.RESULTS: The methods developed had sensitivity ranging from .895 to .992 and positive predictive values (PPV) ranging from .865 to 1 in data sets with low corruption rates. Increased corruption rates lead to decreased sensitivity for all methods.CONCLUSIONS: These new record linkage algorithms show promise in terms of accuracy and efficiency and may be valuable for combining large data sets at the patient level to support biomedical and clinical research.

Resumo Limpo

troduct exist record linkag method handl miss link field valu effici effect manner object studi investig three novel method improv accuraci effici record linkag record linkag field miss valuesmethod extend fellegisunt score implement avail opensourc finegrain record linkag fril softwar system develop three novel method solv miss data problem record linkag refer weight redistribut distanc imput linkag expans weight redistribut remov field miss data set quasiidentifi redistribut weight miss attribut base relat proport across remain avail linkag field distanc imput imput distanc miss data field rather imput miss data valu linkag expans add previous consid nonlinkag field linkag field set compens miss inform linkag field test linkag method use simul data set vari field valu corrupt ratesresult method develop sensit rang posit predict valu ppv rang data set low corrupt rate increas corrupt rate lead decreas sensit methodsconclus new record linkag algorithm show promis term accuraci effici may valuabl combin larg data set patient level support biomed clinic research

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