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. 2010 Apr 27;107(17):7898-903.
doi: 10.1073/pnas.0911686107. Epub 2010 Apr 12.

Anonymization of electronic medical records for validating genome-wide association studies

Grigorios Loukides  1 , Aris Gkoulalas-Divanis, Bradley Malin
Affiliations

Anonymization of electronic medical records for validating genome-wide association studies

Grigorios Loukides et al. Proc Natl Acad Sci U S A. .

Abstract

Genome-wide association studies (GWAS) facilitate the discovery of genotype-phenotype relations from population-based sequence databases, which is an integral facet of personalized medicine. The increasing adoption of electronic medical records allows large amounts of patients' standardized clinical features to be combined with the genomic sequences of these patients and shared to support validation of GWAS findings and to enable novel discoveries. However, disseminating these data "as is" may lead to patient reidentification when genomic sequences are linked to resources that contain the corresponding patients' identity information based on standardized clinical features. This work proposes an approach that provably prevents this type of data linkage and furnishes a result that helps support GWAS. Our approach automatically extracts potentially linkable clinical features and modifies them in a way that they can no longer be used to link a genomic sequence to a small number of patients, while preserving the associations between genomic sequences and specific sets of clinical features corresponding to GWAS-related diseases. Extensive experiments with real patient data derived from the Vanderbilt's University Medical Center verify that our approach generates data that eliminate the threat of individual reidentification, while supporting GWAS validation and clinical case analysis tasks.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Biomedical datasets (fictional) and policies used by the proposed anonymization approach. (A) Research data, (B) identified EMR data, (C) utility policy, (D) privacy policy, and (E) a 5-anonymization for the research data.
Fig. 2.
Fig. 2.
Reidentification risk (shown as a cumulative distribution function).
Fig. 3.
Fig. 3.
Utility constraint satisfaction at various levels of protection for (A) VNEC and (B) VNECKC.
Fig. 4.
Fig. 4.
Relative error in query answering for the single-visit case and for (A) VNEC and (B) VNECKC. Points correspond to the mean RE, and error bars are of 1 SD.
See this image and copyright information in PMC

References

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