Thousands Of Rules Research Articles

Uniqueness of Group Decision Support Systems (GDSS) in medical and health applications

Group Decision Support Systems (GDSS) are defined and discussed. A GDSS model developed by the author is reviewed in depth for communication of the concepts of GDSS. The model's components are related to health care applications. Questions about unique requirements and level of sophistication in health care applications are explored. What are the differences? What is needed in GDSS software? How do implementation strategies differ? The purpose of this paper is to define and discuss the uniqueness and level of sophistication of GDSS applications in health care. The information requirements and level of information abstraction are the major forces considered in the design of specific medical GDSSs. Data for the GDSS and queries originate both internally and externally to the system. Raw data may be in image form and require extensive analysis by the decision makers for information to be extracted from the raw data. Efforts also are made to relate financial and medical data for better business decisions. This integration often has limited success. Additionally, financial data represent multiple sources and present concerns of validity and reliability. In medical diagnoses the knowledge bases are large and contain thousands of rules. Treatment planning and progress reporting rely on medical records that contain thousands of information items and that often require interpretation by an expert. These information attributes go beyond qualitative versus quantitative definitions and are the author's basis for the analysis presented in this paper.

Visual knowledge engineering

The knowledge engineer is only weakly supported at three critical stages in the knowledge engineering life cycle: (1) knowledge acquisition during which problem conceptualization must largely be tackled with paper and pencil; (2) knowledge encoding, during which it is frequently necessary to be able to navigate across a variety of knowledge representation formalisms; and (3) large-scale debugging, in which the graphical rule traces cannot cope with enormous rule sets involving hundreds or thousands of rules. The research described attempts to provide just such support through complementary visual programming (VP) and program visualization (PV) techniques embedded in a fully implemented software environment called KEATS: the knowledge engineer's assistant. Several novel visual programming and program visualization techniques aimed at knowledge engineers have been developed, which include (1) a hypertext transcript analyzer from which conceptual models can be generated, (2) a direct graph manipulation sketchpad which allows the knowledge engineer to sketch out objects and relations (including control flow and rule dependencies) from which code can be generated, and (3) dependency viewers which allow the knowledge engineer to examine and manipulate temporal and logical rule dependencies at different levels of granularity. How these facilities are incorporated into KEATS and the key themes that emerge from this approach to visual knowledge engineering are discussed. >