Evaluation of a Binary Semi-supervised Classification Technique for Probabilistic Record Linkage.

BACKGROUND The process of merging data of different data sources is referred to as record linkage. A medical environment with increased preconditions on privacy protection demands the transformation of clear-text attributes like first name or date of birth into one-way encrypted pseudonyms. When performing an automated or privacy preserving record linkage there might be the need of a binary classification deciding whether two records should be classified as the same entity. The classification is the final of the four main phases of the record linkage process: Preprocessing, indexing, matching and classification. The choice of binary classification techniques in dependence of project specifications in particular data quality has not extensively been studied yet. OBJECTIVES The aim of this work is the introduction and evaluation of an automatable semi-supervised binary classification system applied within the field of record linkage capable of competing or even surpassing advanced automated techniques of the domain of unsupervised classification. METHODS This work describes the rationale leading to the model and the final implementation of an automatable semi-supervised binary classification system and the comparison of its classification performance to an advanced active learning approach out of the domain of unsupervised learning. The performance of both systems has been measured on a broad variety of artificial test sets (n = 400), based on real patient data, with distinct and unique characteristics. RESULTS While the classification performance for both methods measured as F-measure was relatively close on test sets with maximum defined data quality, 0.996 for semi-supervised classification, 0.993 for unsupervised classification, it incrementally diverged for test sets of worse data quality dropping to 0.964 for semi-supervised classification and 0.803 for unsupervised classification. CONCLUSIONS Aside from supplying a viable model for semi-supervised classification for automated probabilistic record linkage, the tests conducted on a large amount of test sets suggest that semi-supervised techniques might generally be capable of outperforming unsupervised techniques especially on data with lower levels of data quality.