ARTIFICIAL INTELLIGENCE CLINICAL DECISION MAKING

我们建议将现有的拨款延续三年， 在不确定环境中改进规范性规定性决策的系统 世界被相互冲突的观点和价值观所困扰。我们尤其 应开发一个环境，以建设和代表 深度决策模型我们将利用现有的系统， 决策树和影响图。我们将提供两个 计算环境和用于描述 决策空间的关系，传统上仍然隐藏在两者 现有的代表。我们将开发一个明确的图形 用于决策上下文的表示，并将替换伪-- 用高级描述的关系的编程表示。在 开发我们将借鉴的建模表示的分类 我们现有的400多个决策分析库， 患者，并将这项研究与一系列应用相结合 项目，目前单独供资，没有足够的灵活性 去探索他们提出的有趣的难题此外，我们亦会制定一套 增强的集成环境，用于在 一般可用的微型计算机系列。我们的制度将实施 在复杂性不断增加的兼容版本中， 中型机器的大型安装基础（80286）。增强的 环境将包括有限内存马尔可夫和Monte Carlo模拟。有了这些环境，我们将探索 这种复杂模型的临床效用和使用 替代分布我们亦会制订一套更佳的方法， 多属性决策问题我们将探索临床和政策 不同优化规则的影响。最后我们继续 扩展我们目前的树批评系统，以捕捉那些决策 错误现在潜伏在伪编程语言中。 这项研究将提高其他研究人员的能力， 规范模式，例如与最近的国家 对结果评估的兴趣和制定准则， 医疗保健支出的增长。该研究的产品将 1）一个系统，用于构建和分析一个广泛的决策类别 问题，2）描述决策问题的语言和术语， （3）加深对决策变量之间关系的理解， 改进规范分析结果的交流技术， 患者、医生和公众。

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.