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2017 ; 28
(2
): 332-352
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Anonymizing and Sharing Medical Text Records
#MMPMID29569650
Li XB
; Qin J
Inf Syst Res
2017[]; 28
(2
): 332-352
PMID29569650
show ga
Health information technology has increased accessibility of health and medical
data and benefited medical research and healthcare management. However, there are
rising concerns about patient privacy in sharing medical and healthcare data. A
large amount of these data are in free text form. Existing techniques for
privacy-preserving data sharing deal largely with structured data. Current
privacy approaches for medical text data focus on detection and removal of
patient identifiers from the data, which may be inadequate for protecting privacy
or preserving data quality. We propose a new systematic approach to extract,
cluster, and anonymize medical text records. Our approach integrates methods
developed in both data privacy and health informatics fields. The key novel
elements of our approach include a recursive partitioning method to cluster
medical text records based on the similarity of the health and medical
information and a value-enumeration method to anonymize potentially identifying
information in the text data. An experimental study is conducted using real-world
medical documents. The results of the experiments demonstrate the effectiveness
of the proposed approach.
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