000			03413nam a22004095i 4500
988			_aSpringer_BiomedLife_2019
999			_c116737 _d116737 _x1
001			116737
003			ES-MaUEC
005			20230104140146.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			190903s2019 gw | s |||| 0|eng d
020			_a9783030199180
024	7		_a10.1007/978-3-030-19918-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aR853.C55 _b2019 EB
100	1		_aCleophas, Ton J. _eautor _986087
245	0	0	_aEfficacy Analysis in Clinical Trials an Update : _bEfficacy Analysis in an Era of Machine Learning _cby Ton J. Cleophas, Aeilko H. Zwinderman.
250			_a1st ed. 2019.
264		1	_aCham, Switzerland _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XI, 304 páginas) _b295 ilustraciones, 44 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aBiomedical and Life Sciences (Springer-11642)
505	0		_aPreface -- Traditional and Machine-Learning Methods for Efficacy Analysis -- Optimal-Scaling for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Complex-Samples for Efficacy Analysis -- Bayesian-Networks for Efficacy Analysis -- Evolutionary-Operations for Efficacy Analysis -- Automatic-Newton-Modeling for Efficacy Analysis -- High-Risk-Bins for Efficacy Analysis -- Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis -- Cluster-Analysis for Efficacy Analysis -- Multidimensional-Scaling for Efficacy Analysis -- Binary Decision-Trees for Efficacy Analysis -- Continuous Decision-Trees for Efficacy Analysis -- Automatic-Data-Mining for Efficacy Analysis -- Support-Vector-Machines for Efficacy Analysis -- Neural-Networks for Efficacy Analysis -- Ensembled-Accuracies for Efficacy Analysis -- Ensembled-Correlations for Efficacy Analysis -- Gamma-Distributions for Efficacy Analysis -- Validation with Big Data, a Big Issue -- Index.
520	3		_aMachine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out multiple variables. Modern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This is the first publication of clinical trials that have been systematically analyzed with machine learning. In addition, all of the machine learning analyses were tested against traditional analyses. Step by step statistics for self-assessments are included. The authors conclude, that machine learning is often more informative, and provides better sensitivities of testing than traditional analytic methods do.
650		7	_2embne _aEnsayos clínicos _9144524
700	1		_aZwinderman, Aeilko H. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _986088
776	0	8	_iPrinted edition: _z9783030199173
776	0	8	_iPrinted edition: _z9783030199197
776	0	8	_iPrinted edition: _z9783030199203
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-19918-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b01/2020 _ek _zSI