Anonymization of Electronic Medical Records to Support Clinical Analysis (Record no. 56419)

000 -LEADER
fixed length control field 03467nam a22005055i 4500
001 - CONTROL NUMBER
control field 978-1-4614-5668-1
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421112037.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 121026s2013 xxu| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781461456681
-- 978-1-4614-5668-1
082 04 - CLASSIFICATION NUMBER
Call Number 502.85
100 1# - AUTHOR NAME
Author Gkoulalas-Divanis, Aris.
245 10 - TITLE STATEMENT
Title Anonymization of Electronic Medical Records to Support Clinical Analysis
300 ## - PHYSICAL DESCRIPTION
Number of Pages XV, 72 p. 23 illus.
490 1# - SERIES STATEMENT
Series statement SpringerBriefs in Electrical and Computer Engineering,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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.
520 ## - SUMMARY, ETC.
Summary, etc 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.
700 1# - AUTHOR 2
Author 2 Loukides, Grigorios.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-1-4614-5668-1
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- New York, NY :
-- Springer New York :
-- Imprint: Springer,
-- 2013.
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-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer science.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Health informatics.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information storage and retrieval.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Science.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Health Informatics.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information Storage and Retrieval.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2191-8112
912 ## -
-- ZDB-2-ENG

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