000			03552nam a22005295i 4500
001			978-1-4614-8785-2
003			DE-He213
005			20140220082832.0
007			cr nn 008mamaa
008			131016s2013 xxu| s |||| 0|eng d
020			_a9781461487852 _9978-1-4614-8785-2
024	7		_a10.1007/978-1-4614-8785-2 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
082	0	4	_a006.312 _223
100	1		_aRakocevic, Goran. _eeditor.
245	1	0	_aComputational Medicine in Data Mining and Modeling _h[electronic resource] / _cedited by Goran Rakocevic, Tijana Djukic, Nenad Filipovic, Veljko Milutinović.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aX, 376 p. 171 illus., 128 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aMining Clinical Data -- Applications of probabilistic and related logics to decision support in medicine -- Transforming electronic medical books to diagnostic decision support systems using relational database management systems -- Text mining in medicine -- A primer on information theory, with applications to neuroscience -- Machine Learning based Imputation of Missing SNP Genotypes in SNP Genotype Arrays -- Computer modeling of atherosclerosis -- Particle dynamics and design of nano-drug delivery systems -- Computational Modeling of Ultrasound Wave Propagation in Bone.
520			_aThis book presents an overview of a variety of contemporary statistical, mathematical and computer science techniques which are used to further the knowledge in the medical domain. The authors focus on applying data mining to the medical domain, including mining the sets of clinical data typically found in patient’s medical records, image mining, medical mining, data mining and machine learning applied to generic genomic data and more. This work also introduces modeling behavior of cancer cells, multi-scale computational models and simulations of blood flow through vessels by using patient-specific models. The authors cover different imaging techniques used to generate patient-specific models. This is used in computational fluid dynamics software to analyze fluid flow. Case studies are provided at the end of each chapter. Professionals and researchers with quantitative backgrounds will find Computational Medicine in Data Mining and Modeling useful as a reference. Advanced-level students studying computer science, mathematics, statistics and biomedicine will also find this book valuable as a reference or secondary text book.
650		0	_aComputer science.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650		0	_aComputer simulation.
650		0	_aBioinformatics.
650	1	4	_aComputer Science.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aComputational Biology/Bioinformatics.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aSimulation and Modeling.
700	1		_aDjukic, Tijana. _eeditor.
700	1		_aFilipovic, Nenad. _eeditor.
700	1		_aMilutinović, Veljko. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461487845
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-8785-2
912			_aZDB-2-SCS
999			_c96086 _d96086