000			03286nam a22003495i 4500
001			317187
003			MX-SnUAN
005			20160429161015.0
007			cr nn 008mamaa
008			160108s2014 gw | s |||| 0|eng d
020			_a9783319041810 _9978-3-319-04181-0
035			_avtls000416398
039		9	_y201601081119 _zstaff
050		4	_aR1
100	1		_aCleophas, Ton J, _eautor. _9308040
245	1	0	_aMachine learning in medicine - cookbook / _cTon J. Cleophas, Aeilko H. Zwinderman.
264		1	_aCham : _bSpringer International Publishing : _bSpringer, _c2014.
300			_axi, 137 páginas : _b14 ilustraciones
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringerBriefs in Statistics, _x2191-544X
500			_aSpringer eBooks
505	0		_aI Cluster Models -- Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys (50 Patients) -- Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data (50 Patients) -- Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future Patients (120 Patients) -- II Linear Models -- Linear, Logistic and Cox Regression for Outcome Prediction with Unpaired Data (20, 55 and 60 Patients) -- Generalized Linear Models for Outcome Prediction with Paired Data (100 Patients and 139 Physicians) -- Generalized Linear Models for Predicting Event-Rates (50 Patients) Exact P-Values -- Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction (250 Patients) -- Optimal Scaling of High-sensitivity Analysis of Health Predictors (250 Patients) -- Discriminant Analysis for Making a Diagnosis from Multiple Outcomes (45 Patients) -- Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread (78 Patients) -- Partial Correlations for Removing Interaction Effects from Efficacy Data (64 Patients) -- Canonical Regression for Overall Statistics of Multivariate Data (250 Patients). III Rules Models -- Neural Networks for Assessing Relationships that are Typically Nonlinear (90 Patients) -- Complex Samples Methodologies for Unbiased Sampling (9,678 Persons) -- Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple Groups (217 Patients) -- Decision Trees for Decision Analysis (1004 and 953 Patients) -- Multidimensional Scaling for Visualizing Experienced Drug Efficacies (14 Pain-killers and 42 Patients) -- Stochastic Processes for Long Term Predictions from Short Term Observations -- Optimal Binning for Finding High Risk Cut-offs (1445 Families) -- Conjoint Analysis for Determining the Most Appreciated Properties of Medicines to Be Developed (15 Physicians) -- Index.
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: _z9783319041803
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-319-04181-0 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942			_c14
999			_c317187 _d317187