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001			288028
003			MX-SnUAN
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007			cr nn 008mamaa
008			150903s2013 xxu| o |||| 0|eng d
020			_a9781461456681 _99781461456681
024	7		_a10.1007/9781461456681 _2doi
035			_avtls000341692
039		9	_a201509030334 _bVLOAD _c201405050232 _dVLOAD _y201402061105 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aR858-R859.7
100	1		_aGkoulalas-Divanis, Aris. _eautor _9313187
245	1	0	_aAnonymization of Electronic Medical Records to Support Clinical Analysis / _cby Aris Gkoulalas-Divanis, Grigorios Loukides.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_axv, 72 páginas 23 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aLoukides, Grigorios. _eautor _9317559
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781461456674
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4614-5668-1 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c288028 _d288028