Cases on Health Outcomes and Clinical Data Mining: Studies and Frameworks

Cases on Health Outcomes and Clinical Data Mining: Studies and Frameworks

Patricia Cerrito
Indexed In: SCOPUS
Release Date: February, 2010|Copyright: © 2010 |Pages: 464
DOI: 10.4018/978-1-61520-723-7
ISBN13: 9781615207237|ISBN10: 1615207236|EISBN13: 9781615207244
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Description & Coverage
Description:

With the healthcare industry becoming increasingly more competitive, there exists a need for medical institutions to improve both the efficiency and the quality of their services. In order to do so, it is important to investigate how statistical models can be used to study health outcomes.

Cases on Health Outcomes and Clinical Data Mining: Studies and Frameworks provides several case studies developed by faculty and graduates of the University of Louisville's PhD program in Applied and Industrial Mathematics. The studies in this book use non-traditional, exploratory data analysis and data mining tools to examine health outcomes, finding patterns and trends in observational data. This book is ideal for the next generation of data mining practitioners.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Data Mining Methodology/Techniques
  • Data Visualization
  • Health Outcomes
  • Market Basket Analysis
  • Medical Expenditure Panel Survey (MEPS)
  • National Inpatient Sample (NIS)
  • Predictive Modeling
  • Survival Analysis
  • Text Analysis
  • Time Series Analysis
Reviews & Statements

The collection of papers illustrates the importance of maintaining close contact between data mining practitioners and the medical community in order to keep a permanent dialogue in order to identify new opportunities for applications of existing data mining technologies.

– Dr. Mehmed Kantardzic, Louisville

This book is successful in emphasizing the role data mining can play in any research conducted from large databases. It could be considered useful as a first step in understanding data mining and its applicability in healthcare research by providing a nice overview of different methods.

– Dr. Daniela Claudia Moga, University of Iowa College of Public Health. Doody's Book Review 2010.
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Editor/Author Biographies
Patricia Cerrito (PhD) has made considerable strides in the development of data mining techniques to investigate large, complex medical data. In particular, she has developed a method to automate the reduction of the number of levels in a nominal data field to a manageable number that can then be used in other data mining techniques. Another innovation of the PI is to combine text analysis with association rules to examine nominal data. The PI has over 30 years of experience in working with SAS software, and over 10 years of experience in data mining healthcare databases. In just the last two years, she has supervised 7 PhD students who completed dissertation research in investigating health outcomes. Dr. Cerrito has a particular research interest in the use of a patient severity index to define provider quality rankings for reimbursements.
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