000			03467nam a22005055i 4500
001			978-1-4614-5668-1
003			DE-He213
005			20200421112037.0
007			cr nn 008mamaa
008			121026s2013 xxu| s |||| 0|eng d
020			_a9781461456681 _9978-1-4614-5668-1
024	7		_a10.1007/978-1-4614-5668-1 _2doi
050		4	_aR858-R859.7
072		7	_aUBH _2bicssc
072		7	_aMED000000 _2bisacsh
082	0	4	_a502.85 _223
100	1		_aGkoulalas-Divanis, Aris. _eauthor.
245	1	0	_aAnonymization of Electronic Medical Records to Support Clinical Analysis _h[electronic resource] / _cby Aris Gkoulalas-Divanis, Grigorios Loukides.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aXV, 72 p. 23 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
505	0		_aIntroduction -- 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			_aAnonymization 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.
650		0	_aComputer science.
650		0	_aHealth informatics.
650		0	_aData mining.
650		0	_aInformation storage and retrieval.
650	1	4	_aComputer Science.
650	2	4	_aHealth Informatics.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aInformation Storage and Retrieval.
700	1		_aLoukides, Grigorios. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461456674
830		0	_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-5668-1
912			_aZDB-2-ENG
942			_cEBK
999			_c56419 _d56419