Machine learning in medicine - a complete overview / (Registro nro. 320539)
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| campo de control de longitud fija | 08063nam a22003375i 4500 |
| 001 - NÚMERO DE CONTROL | |
| campo de control | 320539 |
| 003 - IDENTIFICADOR DEL NÚMERO DE CONTROL | |
| campo de control | MX-SnUAN |
| 005 - FECHA Y HORA DE LA ÚLTIMA TRANSACCIÓN | |
| campo de control | 20160429161425.0 |
| 007 - CAMPO FIJO DE DESCRIPCIÓN FÍSICA--INFORMACIÓN GENERAL | |
| campo de control de longitud fija | cr nn 008mamaa |
| 008 - DATOS DE LONGITUD FIJA--INFORMACIÓN GENERAL | |
| campo de control de longitud fija | 160111s2015 gw | s |||| 0|eng d |
| 020 ## - NÚMERO INTERNACIONAL ESTÁNDAR DEL LIBRO | |
| Número Internacional Estándar del Libro | 9783319151953 |
| -- | 978-3-319-15195-3 |
| 035 ## - NÚMERO DE CONTROL DEL SISTEMA | |
| Número de control de sistema | vtls000419977 |
| 039 #9 - NIVEL DE CONTROL BIBLIOGRÁFICO Y DETALLES DE CODIFICACIÓN [OBSOLETO] | |
| -- | 201601110922 |
| -- | staff |
| 050 #4 - CLASIFICACIÓN DE LA BIBLIOTECA DEL CONGRESO | |
| Número de clasificación | R-RZ |
| 100 1# - ENTRADA PRINCIPAL--NOMBRE DE PERSONA | |
| Nombre de persona | Cleophas, Ton J, |
| Término indicativo de función/relación | autor. |
| 9 (RLIN) | 308040 |
| 245 10 - MENCIÓN DE TÍTULO | |
| Título | Machine learning in medicine - a complete overview / |
| Mención de responsabilidad, etc. | Ton J. Cleophas, Aeilko H. Zwinderman. |
| 264 #1 - PRODUCCIÓN, PUBLICACIÓN, DISTRIBUCIÓN, FABRICACIÓN Y COPYRIGHT | |
| Producción, publicación, distribución, fabricación y copyright | Cham : |
| Nombre del de productor, editor, distribuidor, fabricante | Springer International Publishing : |
| -- | Springer, |
| Fecha de producción, publicación, distribución, fabricación o copyright | 2015. |
| 300 ## - DESCRIPCIÓN FÍSICA | |
| Extensión | xxiv, 516 páginas : |
| Otras características físicas | 159 ilustraciones |
| 336 ## - TIPO DE CONTENIDO | |
| Término de tipo de contenido | texto |
| Código de tipo de contenido | txt |
| Fuente | rdacontent |
| 337 ## - TIPO DE MEDIO | |
| Nombre/término del tipo de medio | computadora |
| Código del tipo de medio | c |
| Fuente | rdamedia |
| 338 ## - TIPO DE SOPORTE | |
| Nombre/término del tipo de soporte | recurso en línea |
| Código del tipo de soporte | cr |
| Fuente | rdacarrier |
| 347 ## - CARACTERÍSTICAS DEL ARCHIVO DIGITAL | |
| Tipo de archivo | archivo de texto |
| Formato de codificación | |
| Fuente | rda |
| 500 ## - NOTA GENERAL | |
| Nota general | Springer eBooks |
| 505 0# - NOTA DE CONTENIDO CON FORMATO | |
| Nota de contenido con formato | Preface. Section I Cluster and Classification 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)- Nearest Neighbors for Classifying New Medicines (2 New and 25 Old Opioids)- Predicting High-Risk-Bin Memberships (1445 Families) -- Predicting Outlier Memberships (2000 Patients) -- Data Mining for Visualization of Health Processes (150 Patients) -- 8 Trained Decision Trees for a More Meaningful Accuracy (150 Patients) -- Typology of Medical Data (51 Patients) -- Predictions from Nominal Clinical Data (450 Patients) -- Predictions from Ordinal Clinical Data (450 Patients) -- Assessing Relative Health Risks (3000 Subjects) -- Measurement Agreements (30 Patients) -- Column Proportions for Testing Differences between Outcome Scores (450 Patients) -- Pivoting Trays and Tables for Improved Analysis of Multidimensional Data (450 Patients) -- Online Analytical Procedure Cubes for a More Rapid Approach to Analyzing Frequencies (450 Patients) -- Restructure Data Wizard for Data Classified the Wrong Way (20 Patients).- Control Charts for Quality Control of Medicines (164 Tablet Disintegration Times) -- Section II (Log) 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).- 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) -- Multinomial Regression for Outcome Categories (55 Patients) -- Various Methods for Analyzing Predictor Categories (60 and 30 Patients) -- Random Intercept Models for Both Outcome and Predictor Categories (55 Patients).- Automatic Regression for Maximizing Linear Relationships (55 Patients) -- Simulation Models for Varying Predictors (9000 Patients) -- Generalized Linear Mixed Models for Outcome Prediction from Mixed Data (20 Patients) -- Two Stage Least Squares for Linear Models with Problematic Predictors (35 Patients) -- Autoregressive Models for Longitudinal Data (120 Monthly Population Records) -- Variance Components for Assessing the Magnitude of Random Effects (40 Patients) -- Ordinal Scaling for Clinical Scores with Inconsistent Intervals (900 Patients) -- Loglinear Models for Assessing Incident Rates with Varying Incident Risks (12 Populations).- Loglinear Models for Outcome Categories (445 Patients) -- Heterogeneity in Clinical Research: Mechanisms Responsible (20 Studies) -- Performance Evaluation of Novel Diagnostic Tests (650 and 588 Patients).- Quantile - Quantile Plots, a Good Start for Looking at Your Medical Data (50 Cholesterol Measurements and 52 Patients) -- Rate Analysis of Medical Data Better than Risk Analysis (52 Patients) -- Trend Tests Will Be Statistically Significant if Traditional Tests Are not (30 and 106 Patients) -- Doubly Multivariate Analysis of Variance for Multiple Observations from Multiple Outcome Variables (16 Patients) -- Probit Models for Estimating Effective Pharmacological Treatment Dosages (14 Tests) -- Interval Censored Data Analysis for Assessing Mean Time to Cancer Relapse (51 Patients).- Structural Equation Modeling with SPSS Analysis of Moment Structures (Amos) for Cause Effect Relationships I (35 Patients) -- Structural Equation Modeling with SPSS Analysis of Moment Structures (Amos) for Cause Effect Relationships II (35 Patients) -- Section 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) -- Item Response Modeling for Analyzing Quality of Life with Better Precision (1000 Patients) -- Survival Studies with Varying Risks of Dying (50 and 60 Patients) -- Fuzzy Logic for Improved Precision of Pharmacological Data Analysis (9 Induction Dosages) -- Automatic Data Mining for the Best Treatment of a Disease (90 Patients) -- Pareto Charts for Identifying the Main Factors of Multifactorial Outcomes (2000 Admissions to Hospital) -- Radial Basis Neural Networks for Multidimensional Gaussian Data (90 persons) -- Automatic Modeling for Drug Efficacy Prediction (250 Patients) -- Automatic Modeling for Clinical Event Prediction (200 Patients) -- Automatic Newton Modeling in Clinical Pharmacology (15 Alfentanil dosages, 15 Quinidine time-concentration relationships) -- Spectral Plots for High Sensitivity Assessment of Periodicity (6 Years’ Monthly C Reactive Protein Levels) -- Runs Test for Identifying Best Analysis Models (21 Estimates of Quantity and Quality of Patient Care) -- Evolutionary Operations for Health Process Improvement (8 Operation Room Settings).- Bayesian Networks for Cause Effect Modeling (600 Patients) -- Support Vector Machines for Imperfect Nonlinear Data -- Multiple Response Sets for Visualizing Clinical Data Trends (811 Patient Visits) -- Protein and DNA Sequence Mining -- Iteration Methods for Crossvalidation (150 Patients) -- Testing Parallel-groups with Different Sample Sizes and Variances (5 Parallel-group Studies) -- Association Rules between Exposure and Outcome (50 and 60 Patients) -- Confidence Intervals for Proportions and Differences in Proportions (100 and 75 Patients) -- Ratio Statistics for Efficacy Analysis of New Drugs 50 Patients).- Fifth Order Polynomes of Circadian Rhythms (1 Patient) -- Gamma Distribution for Estimating the Predictors of Medical Outcomes (110 Patients) Index. |
| 590 ## - NOTA LOCAL (RLIN) | |
| Nota local | Para consulta fuera de la UANL se requiere clave de acceso remoto. |
| 700 1# - PUNTO DE ACCESO ADICIONAL--NOMBRE DE PERSONA | |
| Nombre de persona | Zwinderman, Aeilko H, |
| Término indicativo de función/relación | autor. |
| 9 (RLIN) | 308041 |
| 710 2# - PUNTO DE ACCESO ADICIONAL--NOMBRE DE ENTIDAD CORPORATIVA | |
| Nombre de entidad corporativa o nombre de jurisdicción como elemento de entrada | SpringerLink (Servicio en línea) |
| 9 (RLIN) | 299170 |
| 776 08 - ENTRADA/ENLACE A UN FORMATO FÍSICO ADICIONAL | |
| Información de relación/Frase instructiva de referencia | Edición impresa: |
| Número Internacional Estándar del Libro | 9783319151946 |
| 856 40 - LOCALIZACIÓN Y ACCESO ELECTRÓNICOS | |
| Identificador Uniforme del Recurso | <a href="http://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-319-15195-3">http://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-319-15195-3</a> |
| Nota pública | Conectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL) |
| 942 ## - ELEMENTOS DE PUNTO DE ACCESO ADICIONAL (KOHA) | |
| Tipo de ítem Koha | Recurso en línea |
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