ARTIFICIAL INTELLIGENCE AND CLINICAL DECISION MAKING

这项提议是为了制定一项综合方案，以代表 医学中复杂的知识和推理过程，使用这些技术 人工智能(AI)和决策分析(DA)。该项目 将在两个方面解决诊断和治疗推理方面的问题 医学领域：冠状动脉疾病，以及液体和电解质紊乱。 计划中的大规模人工智能系统将纳入一个共同的知识 表示形式主义和替代推理程序来做数据 解释、假设生成、假设检验和修改， 诊断查询计划、治疗计划和自动生成 解释一下。DA推理将集成到系统中，特别是 完善检测和治疗计划的组成部分。统一的知识 将在两个已确定的医疗领域建立基地，利用 从分析方案、专家意见、 基于案例的学习，从文献中手工编码的知识，并严格 从英语中自动获取结构化知识的有限尝试 自由文本。将开发一组特定的案例，可用于 验证对知识库的添加和推理中的更改 系统的组件。将进行一系列协议分析， 在临床上同时涉及诊断和治疗决策 不确定性和风险是主要因素的环境。特定的 注意力将集中在有经验的临床医生依赖的程度上 将开发基于案例的推理和方法来表示、索引和 在推理程序中利用这种基于案例的经验。一种新的 将建立发展议程工具，以协助构建发展议程模型 在实际的临床环境中使用。这些工具将利用相同的 作为人工智能模型的知识库，并将与他们分享 以不同的深度查看问题并生成对问题的解释 他们的工作方式。一套快速处理的模板分析 进行性肾小球肾炎、肾移植、抗凝剂的使用 心脏瓣膜病的治疗和手术时机，以及 这些领域的风险和预测估计的知识库也将 是被建造的。一种基于人工智能的定量和定性系统 对患者偏好的推理将增强一个有助于健康的系统 专业人士和患者建立个人患者效用 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.