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Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHEJBLUM, Boris
ORCID: 0000-0003-0646-452X
IDREF: 189970316
dc.contributor.authorWEBER, G. M.
dc.contributor.authorLIAO, K. P.
dc.contributor.authorPALMER, N. P.
dc.contributor.authorCHURCHILL, S.
dc.contributor.authorSHADICK, N. A.
dc.contributor.authorSZOLOVITS, P.
dc.contributor.authorMURPHY, S. N.
dc.contributor.authorKOHANE, I. S.
dc.contributor.authorCAI, T.
dc.date.accessioned2020-06-24T07:10:12Z
dc.date.available2020-06-24T07:10:12Z
dc.date.issued2019-01-08
dc.identifier.issn2052-4463 (Electronic) 2052-4463 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/8118
dc.description.abstractEnWe develop an algorithm for probabilistic linkage of de-identified research datasets at the patient level, when only diagnosis codes with discrepancies and no personal health identifiers such as name or date of birth are available. It relies on Bayesian modelling of binarized diagnosis codes, and provides a posterior probability of matching for each patient pair, while considering all the data at once. Both in our simulation study (using an administrative claims dataset for data generation) and in two real use-cases linking patient electronic health records from a large tertiary care network, our method exhibits good performance and compares favourably to the standard baseline Fellegi-Sunter algorithm. We propose a scalable, fast and efficient open-source implementation in the ludic R package available on CRAN, which also includes the anonymized diagnosis code data from our real use-case. This work suggests it is possible to link de-identified research databases stripped of any personal health identifiers using only diagnosis codes, provided sufficient information is shared between the data sources.
dc.language.isoENen_US
dc.subject.enSISTM
dc.title.enProbabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes
dc.title.alternativeSci Dataen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1038/sdata.2018.298en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed30620344en_US
bordeaux.journalScientific Dataen_US
bordeaux.page180298en_US
bordeaux.volume6en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamSISTM_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.exportfalse
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