AN ARTIFICIAL INTELLIGENCE CLINICAL DECISION MAKING

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We propose a three year continuation of our existing grant to develop systems for improved normative prescriptive decision making in an uncertain world beleaguered by conflicting perspectives and values. In particular we shall develop an environment for the construction and representation of deep decision models. We shall draw upon existing systems for building decision trees and influence diagrams. We shall provide both a computational environment and a nomenclature for describing relations in a decision space-relations that have traditionally remained hidden in both existing representations. We shall develop an explicit graphic representation for decision contexts and shall replace the pseudo-- programming representation of relations with high level descriptions. In developing the classification for modeling representation we shall draw on our existing library of over 400 decision analyses performed in individual patients and shall integrate this research with an array of application projects, now funded separately, which do not allow sufficient flexibility to explore the interesting conundrums they raise. We shall also develop an enhanced integrated environment for exploring decision problems on a commonly available family of microcomputers. Our system will be implemented in compatible versions of increasing complexity to allow utilization of the large installed base of moderate size (80286) machines. The enhanced environment will included facilities for limited-Memory Markov and Monte Carlo simulations. With those environments in hand, we shall explore the clinical utility of such complex models and the implications of using alterative distributions. We shall also develop an improved approach to multi-attribute decision problems. We shall explore the clinical and policy implications of different optimization rules. Finally we shall continue expanding our present tree critiquing system to capture those decisional errors now lurking invisibly within the pseudo-programing language. This research will improve the ability of other investigators to develop normative models, such as those associated with the recent national interest in outcome assessment and the establishment of guidelines to limit the growth of health care expenditures. The products of the research will be 1) a system for constructing and analyzing a broad class of decision problems, 2) a language and nomenclature for describing decision problems, and 3) a deeper understanding of the relations among decision variables and improved techniques for communicating the results of normative analyses to patients, physicians and the public.