03499nam 2200565 a 450 991043790880332120200520144314.01-4614-5668-110.1007/978-1-4614-5668-1(CKB)2670000000277830(EBL)1081973(OCoLC)820329418(SSID)ssj0000799364(PQKBManifestationID)11462989(PQKBTitleCode)TC0000799364(PQKBWorkID)10755218(PQKB)11083288(DE-He213)978-1-4614-5668-1(MiAaPQ)EBC1081973(PPN)168303639(EXLCZ)99267000000027783020120827d2013 uy 0engur|n|---|||||txtccrAnonymization of electronic medical records to support clinical analysis /Aris Gkoulalas-Divanis1st ed. 2013.New York Springer20131 online resource (86 p.)SpringerBriefs in electrical and computer engineering,2191-8112Description based upon print version of record.1-4614-5667-3 Includes bibliographical references and index.Introduction -- Overview of patient data anonymization -- Re-identification of clinical data through diagnosis information -- Preventing re-identification while supporting GWAS -- Case study on electronic medical records data -- Conclusions and open research challenges -- Index.Anonymization of Electronic Medical Records to Support Clinical Analysis closely examines the privacy threats that may arise from medical data sharing, and surveys the state-of-the-art methods developed to safeguard data against these threats. To motivate the need for computational methods, the book first explores the main challenges facing the privacy-protection of medical data using the existing policies, practices and regulations. Then, it takes an in-depth look at the popular computational privacy-preserving methods that have been developed for demographic, clinical and genomic data sharing, and closely analyzes the privacy principles behind these methods, as well as the optimization and algorithmic strategies that they employ. Finally, through a series of in-depth case studies that highlight data from the US Census as well as the Vanderbilt University Medical Center, the book outlines a new, innovative class of privacy-preserving methods designed to ensure the integrity of transferred medical data for subsequent analysis, such as discovering or validating associations between clinical and genomic information. Anonymization of Electronic Medical Records to Support Clinical Analysis is intended for professionals as a reference guide for safeguarding the privacy and data integrity of sensitive medical records. Academics and other research scientists will also find the book invaluable.SpringerBriefs in Electrical and Computer Engineering,2191-8112Medical recordsData processingMedical recordsData processing.651.5042610285Gkoulalas-Divanis Aris1058862Loukides Grigorios1755177MiAaPQMiAaPQMiAaPQBOOK9910437908803321Anonymization of electronic medical records to support clinical analysis4191864UNINA