 Privacy-Preserving Record Linkage for Real-World DataTianyu Zhan (),
Yixin Fang and
Weili He
A chapter in Real-World Evidence in Medical Product Development, 2023, pp 109-122 from Springer Abstract: Abstract Real-world data (RWD) are data relating to health status and delivery of health care routinely collected from a variety of sources, including electronic health records (EHRs), claims activities, and patient registries. It is of great interest to aggregate and link data of the same patients from several data sources to provide a more comprehensive longitudinal evaluation of treatments from different aspects. Privacy-Preserving Record Linkage (PPRL) is a framework to match records from different datasets without compromising privacy and confidentiality of these entities. In this chapter, we review several aspects behind PPRL, including data preprocessing, privacy protection, linkage, and performance evaluation. We also provide a demonstration via an existing R package on NHANES (National Health and Nutrition Examination Survey). Keywords: Data linkage; Health care; Performance evaluation (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-26328-6_7 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-26328-6_7 Access Statistics for this chapter More chapters in Springer Books from Springer |
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