000			04027nam a22005175i 4500
001			978-94-007-5824-7
003			DE-He213
005			20140220082939.0
007			cr nn 008mamaa
008			130217s2013 ne | s |||| 0|eng d
020			_a9789400758247 _9978-94-007-5824-7
024	7		_a10.1007/978-94-007-5824-7 _2doi
050		4	_aR-RZ
072		7	_aMBGR _2bicssc
072		7	_aMED000000 _2bisacsh
082	0	4	_a610 _223
100	1		_aCleophas, Ton J. _eauthor.
245	1	0	_aMachine Learning in Medicine _h[electronic resource] / _cby Ton J. Cleophas, Aeilko H. Zwinderman.
264		1	_aDordrecht : _bSpringer Netherlands : _bImprint: Springer, _c2013.
300			_aXV, 265 p. 44 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aPreface -- 1 Introduction to machine learning -- 2 Logistic regression for health profiling -- 3 Optimal scaling: discretization -- 4 Optimal scaling: regularization including ridge, lasso, and elastic net regression -- 5 Partial correlations -- 6 Mixed linear modelling -- 7 Binary partitioning -- 8 Item response modelling -- 9 Time-dependent predictor modelling -- 10 Seasonality assessments -- 11 Non-linear modelling -- 12 Artificial intelligence, multilayer Perceptron modelling -- 13 Artificial intelligence, radial basis function modelling -- 14 Factor analysis -- 15 Hierarchical cluster analysis for unsupervised data -- 16 Partial least squares -- 17 Discriminant analysis for Supervised data -- 18 Canonical regression -- 19 Fuzzy modelling -- 20 Conclusions. Index.                                                                                                                                                                                                                                                                                                                                                .
520			_aMachine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.
650		0	_aMedicine.
650		0	_aComputer vision.
650		0	_aEntomology.
650		0	_aLiteracy.
650		0	_aStatistics.
650	1	4	_aBiomedicine.
650	2	4	_aBiomedicine general.
650	2	4	_aEntomology.
650	2	4	_aMedicine/Public Health, general.
650	2	4	_aStatistics, general.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aLiteracy.
700	1		_aZwinderman, Aeilko H. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9789400758230
856	4	0	_uhttp://dx.doi.org/10.1007/978-94-007-5824-7
912			_aZDB-2-SBL
999			_c99727 _d99727