Anonymization of Electronic Medical Records to Support Clinical Analysis (Record no. 56419)
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000 -LEADER | |
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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. |
336 ## - | |
-- | text |
-- | txt |
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337 ## - | |
-- | computer |
-- | c |
-- | rdamedia |
338 ## - | |
-- | online resource |
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-- | rdacarrier |
347 ## - | |
-- | text file |
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-- | 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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