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

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.
Complete list of metadatas

https://hal.inria.fr/hal-01942279
Contributor : Boris Hejblum <>
Submitted on : Monday, December 3, 2018 - 10:55:05 AM
Last modification on : Friday, October 4, 2019 - 1:32:00 AM
Long-term archiving on : Monday, March 4, 2019 - 1:11:09 PM

File

LinkageAlgorithm_SDR2_V3_HAL.p...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01942279, version 1

Collections

Citation

Boris P. Hejblum, Griffin Weber, Katherine Liao, Nathan Palmer, Susanne Churchill, et al.. Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes. Scientific Data , Nature Publishing Group, In press. ⟨hal-01942279⟩

Share

Metrics

Record views

85

Files downloads

55