This book highlights the interdisciplinary study of cognition, mind and behavior from an information processing perspective, and describes related applications to health informatics. The respective chapters address health problem-solving and education, decision support systems, user-centered interfaces, and the design and use of controlled medical terminologies. Reflecting cutting-edge research on computational methods - including theory, algorithms, numerical simulation, error and uncertainty analysis, and their applications - the book offers a valuable resource for doctoral students and researchers in the fields of Computer Science and Engineering.

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.