Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010236539 | R859.7.D36 C47 2010 f | Open Access Book | Book | Searching... |
On Order
Summary
Summary
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.
Author Notes
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.
