Data Mining in Clinical Medicine

The use of statistics to prove the validity of the treatment over  discrete populations; the creation of predictive models for diagnosis,  prognosis, and treatment; and the inference of clinical guidelines as  decision trees.. the use of the Internet in data mining as well as how  to use distributed data for making better model inferences.

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1. Data Mining: The Essential Guide to Data Mining and Its Potential for Intrusion by David Loshin
2. Data Mining: Techniques and Applications in Intelligent Systems by Michael Berry and Gordon Linoff
3. Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzic
4. Data Mining for Business Intelligence: Concepts, Techniques, and Applications by Galit Shmueli, Nitin R. Patel, Peter C. Bruce
5. Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, Vipin Kumar

This book provides an overview of the use of data mining techniques in clinical medicine. It covers topics such as data pre-processing, feature selection, classification and clustering algorithms, and evaluation methods. The authors discuss how to apply these techniques to medical data sets, including patient records, laboratory results, and imaging studies. They also provide examples of successful applications of data mining in clinical medicine. This book is a valuable resource for researchers and practitioners interested in using data mining to improve healthcare outcomes.