Registries for Evaluating Patient Outcomes

Registries for Evaluating Patient Outcomes
Author: Agency for Healthcare Research and Quality/AHRQ
Publisher: Government Printing Office
Total Pages: 385
Release: 2014-04-01
Genre: Medical
ISBN: 1587634333

This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.

Clustering and Classification

Clustering and Classification
Author: Phipps Arabie
Publisher: World Scientific
Total Pages: 508
Release: 1996
Genre: Mathematics
ISBN: 9789810212872

At a moderately advanced level, this book seeks to cover the areas of clustering and related methods of data analysis where major advances are being made. Topics include: hierarchical clustering, variable selection and weighting, additive trees and other network models, relevance of neural network models to clustering, the role of computational complexity in cluster analysis, latent class approaches to cluster analysis, theory and method with applications of a hierarchical classes model in psychology and psychopathology, combinatorial data analysis, clusterwise aggregation of relations, review of the Japanese-language results on clustering, review of the Russian-language results on clustering and multidimensional scaling, practical advances, and significance tests.

Applied Health Analytics and Informatics Using SAS

Applied Health Analytics and Informatics Using SAS
Author: Joseph M. Woodside
Publisher:
Total Pages: 378
Release: 2018-11
Genre: Computers
ISBN: 9781642953404

Leverage health data into insight! Applied Health Analytics and Informatics Using SAS describes health anamatics, a result of the intersection of data analytics and health informatics. Healthcare systems generate nearly a third of the world's data, and analytics can help to eliminate medical errors, reduce readmissions, provide evidence-based care, demonstrate quality outcomes, and add cost-efficient care. This comprehensive textbook includes data analytics and health informatics concepts, along with applied experiential learning exercises and case studies using SAS Enterprise MinerTM within the healthcare industry setting. Topics covered include: Sampling and modeling health data - both structured and unstructured Exploring health data quality Developing health administration and health data assessment procedures Identifying future health trends Analyzing high-performance health data mining models Applied Health Analytics and Informatics Using SAS is intended for professionals, lifelong learners, senior-level undergraduates, graduate-level students in professional development courses, health informatics courses, health analytics courses, and specialized industry track courses. This textbook is accessible to a wide variety of backgrounds and specialty areas, including administrators, clinicians, and executives.

Data Analysis and Informatics, V

Data Analysis and Informatics, V
Author: E. Diday
Publisher: North Holland
Total Pages: 538
Release: 1988
Genre: Computers
ISBN: 9780444704450

New theoretical developments and numerous industrial applications of Data Analysis and Informatics are presented in this volume. The theoretical results include advances in multiway tables, heterogeneous tables, textual data, validation, stability of methods, robustness and quality of developments. On the strategic level, different styles of data analysis are also shown. Several papers deal with the facilities now available due to the progress made in graphics and artificial intelligence. The industrial applications include scintigraphic image series processing and image improving, VLSI, assistance in survey processing by expert systems, the implementation of production systems, and electrical network planning.

Recent Developments in Clustering and Data Analysis

Recent Developments in Clustering and Data Analysis
Author: Chikio Hayashi
Publisher: Academic Press
Total Pages: 469
Release: 2014-05-10
Genre: Mathematics
ISBN: 1483263096

Recent Developments in Clustering and Data Analysis presents the results of clustering and multidimensional data analysis research conducted primarily in Japan and France. This book focuses on the significance of the data itself and on the informatics of the data. Organized into four sections encompassing 35 chapters, this book begins with an overview of the quantification of qualitative data as a method of analyzing statistically multidimensional data. This text then examines the rules of interpretation of correspondence cluster analysis by selecting classes and explaining variables involved in the algorithm of hierarchical classification. Other chapters consider the bootstrap and cross-validation methods, which are applied to the logistic ad nonparametric regression analyses of ordered categorical responses. The final chapter deals with a simpler treatment to classify the sleep state. This book is a valuable resource for researchers and workers in the fields from the behavioral sciences, biological sciences, medicine, and industrial sciences.

Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering

Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering
Author: Israël César Lerman
Publisher: Springer
Total Pages: 664
Release: 2016-03-24
Genre: Computers
ISBN: 1447167937

This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.