Data Mining and Analytics in Healthcare Management

David L. Olson is the James & H.K. Stuart Professor in MIS and Chancellor’s Professor at the University of Nebraska-Lincoln, USA. He has published research in over 200 refereed journal articles and has authored over 40 books. He has served as associate editor of a number of journals and made hundreds of presentations at international and national conferences on research topics. He is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was a Lowry Mays endowed Professor at Texas A&M University from 1999 to 2001. He was named Best Enterprise Information Systems Educator by the IFIP in 2006. He is a Fellow of the Decision Sciences Institute.Dr. Özgür Araz is the Ron and Carol Cope Professor and Professor of Supply Chain Management and Analytics at the University of Nebraska-Lincoln, USA. His research interests include systems simulation, business analytics, healthcare operations and public health informatics. His research has been supported by the NIH, Veterans Engineering Resource Center (VERC), HDR company, Boys Town of Nebraska, Nebraska Medicine and the University of Nebraska. Before joining the College of Business at UNL, he served at the College of Public Health at the University of Nebraska Medical Center (UNMC). He received his Ph.D. in Industrial Engineering from Arizona State University and was a postdoctoral research fellow at the Center for Computational Biology and Bioinformatics of the University of Texas at Austin. He is an editorial advisory board member of the Transportation Research Part E and also serves as associate editor for Decision Sciences and IISE Transactions on Healthcare Systems Engineering. He is the Public Health Informatics Area Editor for the journal Health Systems. He is also a faculty fellow of the Nebraska Governance and Technology Center and Daugherty Water for Food Global Institute.

• Chapter 1: Urgency in Healthcare Data Analytics.- Chapter 2: Analytics and Knowledge Management in Healthcare.- Chapter 3: Visualization.- Chapter 4: Association Rules.- Chapter 5: Cluster Analysis.- Chapter 6: Time Series Forecasting.- Chapter 7: Classification Models.- Chapter 8: Applications of Predictive Data Mining in Healthcare.- Chapter 9: Decision Analysis and Applications in Healthcare.- Chapter 10: Analysis of Four Medical Datasets.- Chapter 11: Multiple Criteria Decision Models in Healthcare- Chapter 12: Naïve Bayes Models in Healthcare.- Chapter 13: Summation