ARTIFICIAL INTELLIGENCE AND CLINICAL DECISION MAKING

该提案旨在制定一项综合方案， 医学中复杂的知识和推理过程， 人工智能（AI）和决策分析（DA）。 项目 将在两个方面解决诊断和治疗推理中的问题 医学领域：冠状动脉疾病、液体和电解质紊乱。 计划中的大规模人工智能系统将融入常识 表示形式主义和替代推理程序做数据 解释、假设生成、假设检验和修正， 诊断查询计划、治疗计划和自动生成的 解释。 DA推理将被集成到系统中，特别是 以改进测试和治疗计划组件。 统一的知识 将在两个确定的医疗领域建立一个基础，利用 从方案分析中获得的知识，专家意见， 基于案例的学习，从文献中手工编码知识，并严格 从英语中自动获取结构化知识的尝试有限 自由文本。 将制定一组具体案例， 验证知识库的添加和推理中的更改 系统的组成部分。 将进行一系列方案分析， 涉及临床诊断和治疗决策 不确定性和风险是主要因素的环境。 具体 注意力将集中在有经验的临床医生依赖于 将开发基于案例的推理和方法，以表示、索引和 在推理程序中利用这种基于案例的经验。 一个新 将建立新一代的DA工具来辅助DA模型的构建 用于实际临床环境。 这些工具将利用相同的 知识库作为人工智能模型，并将与他们分享的能力， 以不同的细节深度来看待问题，并对问题进行解释。 他们的工作 一套快速处理 进行性肾小球肾炎，肾移植，抗凝剂应用 心脏瓣膜病的治疗和手术时机，以及 这些领域风险和预后估计的知识基础也将 被建造。 一个基于人工智能的定量和定性系统 对病人偏好的推理将增强一个系统， 专业人员和患者建立个体患者效用 结构.

This proposal is to develop an integrated program for the representation of complex knowledge and reasoning processes in medicine, using the techniques of artificial intelligence (AI) and decision analysis (DA). The project will address problems in both diagnostic and therapeutic reasoning in two medical domains: coronary disease, and fluid and electrolyte disturbances. The planned large-scale AI system will incorporate a common knowledge representation formalism and alternative reasoning programs to do data interpretation, hypothesis generation, hypothesis testing and revision, diagnostic query planning, therapeutic planning and automatically-generated explanations. DA reasoning will be integrated into the system, especially to improve the test and treatment planning components. A uniform knowledge base will be developed in the two identified medical domains, utilizing knowledge acquired from the analysis of protocols, expert opinion, case-based learning, hand-coded knowledge from the literature, and strictly limited attempts to automatically acquire structured knowledge from English free-text. A panel of specific cases will be developed that can be used to validate additions to the knowledge base and changes in the reasoning components of the system. A series of protocol analyses will be done, involving both diagnostic and therapeutic decision making in clinical settings where uncertainty and risk are predominant factors. Specific attention will focus on the degree to which experienced clinicians rely on case-based reasoning and means will be developed to represent, index and utilize such case-based experience in the reasoning program. A new generation of DA tools will be built to aid the construction of DA models for use in actual clinical settings. These tools will utilize the same knowledge bases as the AI models, and will share with them the ability to look in varying depth of detail at problems and to generate explanations of their workings. A set of template analyses dealing with rapidly progressive glomerulonephritis, renal transplantation, use of anticoagulant therapy and the timing of surgery for valvular heart disease, and a knowledge base of risk and prognosis estimates in these domains will also be constructed. An AI based system for quantitative and qualitative reasoning about patient preferences will augment a system to help health professionals and patients to establish individual patient utility structures.