Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes
Name
sdata2018298.pdf
Description
Published version
Size
804.8 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Hejblum, Boris P.
Weber, Griffin M.
Liao, Katherine P.
Palmer, Nathan P.
Churchill, Susanne
Shadick, Nancy A.
Szolovits, Peter
Murphy, Shawn N.
Kohane, Isaac S.
Cai, Tianxi
Date Issued
January 2019
Journal
Scientific Data
Publisher
Springer Nature
Citation
Hejblum, Boris P. et al. "Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes." Scientific Data 6 (2019): 180298 © 2019 The Author(s)
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/sdata.2018.298
