AN ARTIFICIAL INTELLIGENCE CLINICAL DECISION MAKING

人工智能临床决策

• 批准号: 3373768
• 负责人: PETER SZOLOVITS
• 金额: $ 45.67万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 1985
• 资助国家: 美国
• 起止时间: 1985-09-30 至 1993-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3373768
• 关键词: abstracting; acute disease /disorder; artificial intelligence; case history; computer assisted medical decision making; computer simulation; computer system design /evaluation; diabetes mellitus; disease /disorder classification; fibrinolytic agents; heart disorder chemotherapy; information systems; information theory; literature survey; model design /development; myocardial ischemia /hypoxia; pain; patient care management; stomach disorder

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.

我们建议将现有赠款延续三年，以开发 在不确定的情况下改进规范性决策的系统 世界被相互冲突的观点和价值观所困扰。特别是我们 应营造一个有利于建设和代表的环境 深度决策模型。我们将利用现有的系统来构建 决策树和影响图。我们将提供 计算环境和描述关系的术语 传统上隐藏在两者中的决策空间关系 现有的陈述。我们将开发一个明确的图形 决策上下文的表示，并应取代伪-- 具有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分类 我们现有的库包含 400 多项单独进行的决策分析 患者，并将这项研究与一系列应用相结合 现在单独资助的项目，缺乏足够的灵活性 探索他们提出的有趣难题。我们还将开发一个 增强的集成环境，用于探索决策问题 常用的微型计算机系列。我们的系统将被实施 在复杂性不断增加的兼容版本中，以允许利用 中等尺寸 (80286) 机器的大型安装基础。增强型 环境将包括有限记忆马尔可夫和蒙特的设施 卡罗模拟。有了这些环境，我们将探索 这种复杂模型的临床实用性以及使用的意义 替代分布。我们还将开发一种改进的方法 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。最后我们还要继续 扩展我们现有的树木批评系统以捕获那些决策性的 错误现在隐藏在伪编程语言中。 这项研究将提高其他研究人员开发的能力 规范模型，例如与最近的国家 对结果评估和制定限制指南的兴趣 医疗保健支出的增长。该研究的产品将 1）用于构建和分析广泛决策的系统 问题，2）用于描述决策问题的语言和术语， 3）更深入地理解决策变量之间的关系 改进了将规范分析结果传达给其他人的技术 患者、医生和公众。