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

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

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 pr...

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Bibliographic Details
Main Authors: 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
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:English
Published: Springer Nature 2019
Online Access:https://hdl.handle.net/1721.1/122815

Internet

https://hdl.handle.net/1721.1/122815

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