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Sci Data. 2019 Jan 8;6:180298. doi: 10.1038/sdata.2018.298.

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

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Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
University Bordeaux, ISPED, Inserm Bordeaux Population Health Research Center, UMR 1219, Inria SISTM, Bordeaux F-33000, France.
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.
Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Research IS and Computing, Partners HealthCare, Charlestown, MA, USA.


We 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.

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