Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis

Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis

Release Date: June, 2010|Copyright: © 2010 |Pages: 370
DOI: 10.4018/978-1-61520-905-7
ISBN13: 9781615209057|ISBN10: 1615209050|EISBN13: 9781615209064
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Description & Coverage
Description:

The investigation of healthcare databases can be used to examine physician decisions and develop evidence-based treatment guidelines that optimize patient outcomes.

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.

Coverage:

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

  • Co-morbidities
  • Dealing with large datasets
  • Defining patient severity indices
  • Estimating probabilities of treatment outcomes
  • Improving Patient Care
  • Patient Compliance
  • Preprocessing of large, healthcare databases
  • Relationship between treatment and outcome
  • Time series methods
  • Use of data mining techniques to investigate data
Reviews & Statements

It is our interest in writing this book to demonstrate how concern for details in the datasets, and the use of data mining techniques can extract important and meaningful knowledge from the data. We anticipate that this knowledge can be used to improve patient care.

– Patricia and John Cerrito

This book is a valuable contribution, which will help researchers understand issues related to research using large healthcare databases. I'm not aware of other books that cover these important topics and provide such detailed analytic examples. As far as the book's usefulness to researchers with SAS experience who are working with administrative healthcare databases, it is an excellent resource, and I plan to add it to my library.

– Ryan M Carnahan, Pharm.D., M.S., University of Iowa College of Public Health, Doody's Book Review

If you are a researcher or healthcare professional interested in exploring MEPS, the National Inpatient Sample or Medpar databases, have access to SAS Enterprise Guide and Enterprise Miner, and are interested in learning about how data mining tools can be applied to extract knowledge from these specific databases, then you should consider buying this book.

– Fernando Bacao, Universidade Nova de Lisboa, Online Information Review
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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.
John Cerrito has practiced pharmacy for over 30 years. He currently is a doctor of pharmacy practicing retail and consulting pharmacy. He has considerable expertise in drug interactions and in working with healthcare claims data to investigate health outcomes.
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