ARTIFICIAL INTELLIGENCE CLINICAL DECISION MAKING

我们建议将现有的赠款续期三年，以开发 在不确定的情况下改进规范规范决策的系统 世界被相互冲突的观点和价值观所困扰。尤其是我们 应发展一个环境，以建设和代表 深度决策模型。我们将利用现有的系统进行建设 决策树和影响图。我们将同时提供一个 计算环境和描述关系的命名法 决策空间-传统上隐藏在这两个空间中的关系 现有的表示法。我们将制作一张清晰的图表 用于决策上下文的表示，并应取代伪- 用高级描述对关系进行编程表示。在……里面 开发我们将利用的建模表示的分类 我们现有的包含400多项决策分析的资料库 并将这项研究与一系列应用程序相结合 现在单独出资的项目，不允许有足够的灵活性 来探索他们提出的有趣的谜题。我们还将开发一种 增强的集成环境，用于探索 常见的微型计算机系列。我们的系统将被实施 在日益复杂的兼容版本中，以允许使用 中等规模(80286台)机器的大型客户群。增强版 环境将包括用于有限内存的马尔可夫和蒙特卡罗的设施 卡罗模拟。掌握了这些环境之后，我们将探索 这类复杂模型的临床实用性及其使用的意义 交替分布。我们还将制定一种改进的方法来 多属性决策问题。我们将探讨临床和政策 不同优化规则的含义。最后，我们将继续 扩展我们目前的树木评论系统，以捕捉这些决策 错误现在潜伏在伪编程语言中，看不见。 这项研究将提高其他研究人员的开发能力 规范性模式，例如与最近的国家 对结果评估的兴趣和制定准则以限制 医疗保健支出的增长。这项研究的成果将 BE 1)用于构建和分析大类决策的系统 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认识 改进了将规范分析的结果传达给 病人、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.