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dc.contributor.authorStausberg, J.
dc.contributor.authorWaldenburger, A.
dc.contributor.authorBorgs, Christian
dc.contributor.authorSchnell, Rainer
dc.identifier.citationStausberg, J. and Waldenburger, A. and Borgs, C. and Schnell, R. 2017. Combining Different Privacy-Preserving Record Linkage Methods for Hospital Admission Data. Studies in Health Technology and Informatics. 235: pp. 161-165.

© 2017 European Federation for Medical Informatics (EFMI) and IOS Press. Record linkage (RL) is the process of identifying pairs of records that correspond to the same entity, for example the same patient. The basic approach assigns to each pair of records a similarity weight, and then determines a certain threshold, above which the two records are considered to be a match. Three different RL methods were applied under privacy-preserving conditions on hospital admission data: deterministic RL (DRL), probabilistic RL (PRL), and Bloom filters. The patient characteristics like names were one-way encrypted (DRL, PRL) or transformed to a cryptographic longterm key (Bloom filters). Based on one year of hospital admissions, the data set was split randomly in 30 thousand new and 1,5 million known patients. With the combination of the three RL-methods, a positive predictive value of 83 % (95 %-confidence interval 65 %-94 %) was attained. Thus, the application of the presented combination of RL-methods seem to be suited for other applications of population-based research.

dc.publisherIOS Press
dc.titleCombining Different Privacy-Preserving Record Linkage Methods for Hospital Admission Data
dc.typeJournal Article
dcterms.source.titleStudies in Health Technology and Informatics
curtin.departmentCentre for Population Health Research
curtin.accessStatusFulltext not available

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