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020			_a9789400758247 _99789400758247
024	7		_a10.1007/9789400758247 _2doi
035			_avtls000367682
039		9	_a201509031046 _bVLOAD _c201405070445 _dVLOAD _y201402251630 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aR-RZ
100	1		_aCleophas, Ton J. _eautor _9308040
245	1	0	_aMachine Learning in Medicine / _cby Ton J. Cleophas, Aeilko H. Zwinderman.
264		1	_aDordrecht : _bSpringer Netherlands : _bImprint: Springer, _c2013.
300			_axv, 265 páginas 44 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aZwinderman, Aeilko H. _eautor _9308041
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9789400758230
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-94-007-5824-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c313630 _d313630