Operations Research
Bottom-up induction of oblivious read-once decision graphs
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
Computers and Operations Research - Special issue on artificial intelligence and decision support with multiple criteria
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Knowledge refinement based on the discovery of unexpected patterns in data mining
Decision Support Systems - Special issue: Formal modeling and electronic commerce
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Representing and Solving Decision Problems with Limited Information
Management Science
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
A review of explanation methods for heuristic expert systems
The Knowledge Engineering Review
The effects of structural characteristics of explanations on use of a DSS
Decision Support Systems
A framework for supporting emergency messages in wireless patient monitoring
Decision Support Systems
A Personal Assistant for Autonomous Life
HCD 09 Proceedings of the 1st International Conference on Human Centered Design: Held as Part of HCI International 2009
Efficiency of influence diagram models with continuous decision variables
Decision Support Systems
Dealing with complex queries in decision-support systems
Data & Knowledge Engineering
A framework for enabling patient monitoring via mobile ad hoc network
Decision Support Systems
Two machine-learning techniques for mining solutions of the ReleasePlannerTM decision support system
Information Sciences: an International Journal
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When solving clinical decision-making problems with modern graphical decision-theoretic models such as influence diagrams, we obtain decision tables with optimal decision alternatives describing the best course of action for a given patient or group of patients. For real-life clinical problems, these tables are often extremely large. This is an obstacle to understand their content. KBM2L lists are structures that minimize memory storage requirements for these tables, and, at the same time, improve their knowledge organization. The resulting improved knowledge organization can be interpreted as explanations of the decision-table content. In this paper, we explore the use of KBM2L lists in analyzing and explaining optimal treatment selection in patients with non-Hodgkin lymphoma of the stomach using an expert-designed influence diagram as an experimental vehicle. The selection of the appropriate treatment for non-Hodgkin lymphoma of the stomach is, as for many other types of cancer, difficult, mainly because of the uncertainties involved in the decision-making process. In this paper we look at an expert-designed clinical influence diagram as a representation of a body of clinical knowledge. This diagram can be analyzed and explained using KBM2L lists. It is shown that the resulting lists provide high-level explanations of optimal treatments for the disease. These explanations are useful for finding relationships between groups of variables and treatments. It is demonstrated that these lists can act as a basis for gaining a deeper understanding of the underlying clinical problem.