title: Analysis of a probabilistic record linkage technique without human review.
abstract: We previously developed a deterministic record linkage algorithm demonstrating sensitivities approaching 90% while maintaining 100% specificity. Substantially better performance has been reported using probabilistic linkage techniques; however, such methods often incorporate human review into the process. To avoid human review, we employed an estimator function using the Expectation Maximization (EM) algorithm to establish a single true-link threshold. We compared the unsupervised probabilistic results against the manually reviewed gold-standard for two hospital registries, as well against our previous deterministic results. At an estimated specificity of 99.95%, actual specificities were 99.43% and 99.42% for registries A and B, respectively. At an estimated sensitivity of 99.95%, actual sensitivities were 99.19% and 98.99% for registries A and B, respectively. The EM algorithm estimated linkage parameters with acceptable accuracy, and was an improvement over the deterministic algorithm. Such a methodology may be used where record linkage is required, but human intervention is not possible or practical.
authors.affiliation: Regenstrief Institute and Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Shaun J
authors.initials: SJ
authors.lastname: Grannis
authors.forename: J Marc
authors.initials: JM
authors.lastname: Overhage
authors.forename: Siu
authors.initials: S
authors.lastname: Hui
authors.forename: Clement J
authors.initials: CJ
authors.lastname: McDonald
mesh_terms: D000465:Algorithms; D003195:Computer Communication Networks; D006801:Humans; D008433:Mathematics; D008498:Medical Record Linkage / Q000379:methods*; D016347:Medical Records Systems, Computerized; D011336:Probability