ARTIFICIAL INTELLIGENCE AND CLINICAL DECISION MAKING

人工智能与临床决策

• 批准号: 3373765
• 负责人: PETER SZOLOVITS
• 金额: $ 96.84万
• 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 1990-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3373765
• 关键词: abstracting; artificial intelligence; body water; case history; computer assisted medical decision making; computer simulation; computer system design /evaluation; coronary disorder; electrolyte balance; expectancy; heart valve disorder; information systems; information theory; problem solving; renal failure

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.

该提案旨在制定一项综合计划，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）。 项目 将解决两个方面的诊断和治疗推理问题 医学领域：冠心病、液体和电解质紊乱。 计划中的大规模人工智能系统将包含共同知识 表示形式主义和替代推理程序来处理数据 解释、假设生成、假设检验和修正、 诊断查询规划、治疗规划并自动生成 解释。 DA推理将被集成到系统中，特别是 改进测试和治疗计划的组成部分。 统一的知识 将在两个确定的医疗领域开发基地，利用 从协议分析、专家意见中获得的知识， 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。 将开发一组具体案例，可用于 验证知识库的添加和推理的变化 系统的组成部分。 将进行一系列协议分析， 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。 具体的 注意力将集中在经验丰富的临床医生的依赖程度 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。 一个新的 将构建新一代 DA 工具来帮助构建 DA 模型 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，并将与他们分享能力 以不同深度的细节来看待问题并给出解释 他们的工作。 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 瓣膜性心脏病的治疗和手术时机，以及 这些领域的风险和预后估计的知识库也将 被建造。 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有助于健康的系统 专业人员和患者建立个人患者效用 结构。