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.

该建议是为代表的综合程序制定集成程序 使用这些技术，医学中的复杂知识和推理过程 人工智能（AI）和决策分析（DA）。 项目 将解决两者的诊断和治疗推理中的问题 医疗领域：冠状动脉疾病，液体和电解质障碍。 计划中的大规模AI系统将结合常识 代表形式主义和进行数据的替代推理计划 解释，假设产生，假设检验和修订， 诊断查询计划，治疗计划和自动生成 解释。 DA推理将集成到系统中，尤其是 改善测试和治疗计划组件。 统一的知识 基础将在两个已确定的医疗领域中开发 从协议，专家意见分析中获得的知识， 基于案例的学习，从文献中进行了手工编码的知识以及严格的 有限的尝试从英语中自动获取结构化知识的尝试有限 自由文本。 将开发一组特定案例的面板 验证知识库中的增加和推理的变化 系统的组件。 将进行一系列协议分析， 在临床上涉及诊断和治疗决策 不确定性和风险是主要因素的设置。 具体的 注意将集中于经验丰富的临床医生依赖的程度 将开发基于案例的推理和手段来代表索引和 在推理计划中利用这种基于案例的经验。 一个新 将生成DA工具以帮助建造DA模型 用于实际临床环境。 这些工具将使用相同的 知识库作为AI模型，并将与他们分享 在各种细节中查看问题，并产生解释 他们的工作。 一组模板分析快速处理 进行性肾小球肾炎，肾移植，使用抗凝剂 治疗和瓣膜心脏病的手术时间和 这些领域中风险和预后估计的知识基础也将 被构造。 基于AI的定量和定性系统 关于患者偏好的推理将增加系统以帮助健康 专业人士和患者建立个体患者公用事业 结构。