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 pack

SUBMITTER: Hejblum BP 

PROVIDER: S-EPMC6326114 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature