A Bayesian Model Framework to Determine Patient Compliance in Glaucoma Cases

About the Book

This research applied Bayesian modeling to medication noncompliance in glaucoma patients. A model-based decision support system using a Bayesian Network was developed to determine whether a patient was complying with the medications prescribed by the physician. Results from this study could potentially improve the decision making process, given the uncertain and incomplete data available to a physician. The model may be generalized to other business situations where a decision has to be made based on incomplete and uncertain data sets.

Bayesian Networks have increasingly become tools of choice in solving problems involving uncertainty in the medical domain. These models have been successfully applied to diagnosis applications. The purpose of this research was to devise a Bayesian framework to assess the compliance with medication in glaucoma patients.

About the Author

Dr. Korrapati holds a Ph.D degree in Computer Information Systems, an M.B.A. in Business Administration, M.S. in Computer Science, M.Tech. in Computer Science and Technology and B.S. in Engineering. He is a Charted Property and Casualty Underwriter (CPCU), Certified Project Management Professional (PMP), and Certified Microsoft Professional (MCP).

Dr. Korrapati teaches for Masters and Ph.D students in Business, Management, and Computer Science. He is a mentor in Applied Management and Decision Sciences (AMDS) program where he advises Ph.D students on research activities and teaching. Dr. Korrapati has published a number of articles in peer-reviewed conferences and journals in the areas of business, management, and computer information systems.

He is a peer reviewer for various Conferences and serves on Editorial Boards on several peer-reviewed journals. He is the Editor-In-Chief of a peer-reviewed journal?International Journal of Applied Management and Technology (http://www.iJAMT.org/) and Conference Manager for Annual Conference on Applied Management and Decision Sciences (http://www.amdsconference.org/).