Clinical data mining for physician decision making and investigating health outcomes methods for prediction and analysis Patricia Cerrito, John Cerrito, editors.
Detalles de publicación: Hershey, PA Medical Information Science Reference c2010.Descripción: xiv, 356 p. ill. 29 cmISBN:- 9781615209057 (h/c)
- 1615209050 (h/c)
- 610.285 C6413 22
- R859.7.D36 C47 2010
- W 26.55.I4
| Imagen de cubierta | Tipo de ítem | Biblioteca actual | Biblioteca de origen | Colección | Ubicación en estantería | Signatura topográfica | Materiales especificados | Info Vol | URL | Copia número | Estado | Notas | Fecha de vencimiento | Código de barras | Reserva de ítems | Prioridad de la cola de reserva de ejemplar | Reservas para cursos | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Libro | Biblioteca Encarnación Valdés Colección General bev | 610.285 C6413 (Navegar estantería(Abre debajo)) | Disponible | 80000002491119 |
Descripciones mejoradas de Syndetics:
Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis demonstrates how concern for detail in datasets and the use of data mining techniques can extract important and meaningful knowledge from healthcare databases. Basic information on processing data with step-by-step instructions is provided, allowing readers to use their own data and follow the instructions to find meaningful results.
"Premier Reference Source" --Cover.
Includes bibliographical references and index.
Preprocessing the data -- Errors and missing values in the dataset -- Introduction to the use of MEPS (medical expenditure panel survey) -- Preprocessing Medpar data -- Extracting data from the national inpatient sample -- Creating a one-to-one relationship in the data from a many-to-many -- Merging different datasets to allow for a complete analysis (inpatient, outpatient, physician visits, medications) -- Introduction to analysis using time components -- More survival data mining of multiple time of endpoints -- Using the data to define patient compliance -- Compression of diagnosis and procedure codes -- Comparisons of patient severity indices -- Decision trees and their development : use of data to determine the quality of care -- Example of diabetes using CMS data -- Example of breathing illness, asthma and COPD using MEPS data -- Example of wound care using Medpar data.
"This book shows how the investigation of healthcare databases can be used to examine physician decisions to develop evidence-based treatment guidelines that optimize patient outcomes"--Provided by publisher.
Tabla de contenidos provista por Syndetics
- 1 Preprocessing the Data(p. 1)
- 2 Errors and Missing Values in the Dataset(p. 11)
- 3 Introduction to the Use of MEPS (Medical Expenditure Panel Survey)(p. 19)
- 4 Preprocessing Medpar Data(p. 57)
- 5 Extracting Data from the National Inpatient Sample(p. 69)
- 6 Creating a One-to-One Relationship in the Data from a Many-to-Many(p. 94)
- 7 Merging Different Datasets to Allow for a Complete Analysis (Inpatient, Outpatient, Physician Visits, Medications)(p. 116)
- 8 Introduction to Analysis Using Time Components(p. 154)
- 9 More Survival Data Mining of Multiple Time of Endpoints(p. 193)
- 10 Using the Data to Define Patient Compliance(p. 215)
- 11 Compression of Diagnosis and Procedure Codes(p. 234)
- 12 Comparisons of Patient Severity Indices(p. 249)
- 13 Decision Trees and Their Development: Use of Data to Determine the Quality of Care(p. 287)
- 14 Example of Diabetes Using CMS Data(p. 305)
- 15 Example of Breathing Illnesses, Asthma and COPD Using MEPS Data(p. 318)
- 16 Example of Wound Care Using Medpar Data(p. 329)
- 17 Discussion(p. 346)