COMPUTER-BASED EXPLANATION METHODS FOR DECISION MODELS

基于计算机的决策模型解释方法

• 批准号: 2430874
• 负责人: CURTIS P LANGLOTZ
• 金额: $ 10.63万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-06-01 至 2001-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2430874
• 关键词: artificial intelligence; computer assisted medical decision making; computer program /software; computer system design /evaluation; health care model; health care service evaluation; human subject; mathematical model; questionnaires

There is growing recognition that, while practice guidelines can predispose physicians to behavioral change, even the most respected and clinically relevant guidelines often do not affect actual practice. Despite the apparent reluctance of physicians to incorporate published practice guidelines into their clinical routine, there are signs that immediate automated reminders can have a profound influence on physician behavior, and on the cost and quality of medical care. As practice guidelines proliferate and become more complex, the dilemma facing those who design practice guidelines is the tradeoff between the comprehensiveness of the guidelines and the likelihood they will be retained and implemented by the physicians for whom they are intended. This same complexity can complicate computer-based implementation of guidelines. For example, the dependence of guidelines on patient preferences and individual risk factors makes the rule-based approach impractical for many guidelines. Intelligent decision systems (IDS) are specifically designed to implement complex, flexible guidelines tailored to varying clinical circumstances. These systems provide advice based on a tailored decision-analytic model. But these systems assume that a physician-user will be persuaded by a review of the decision model and its associated quantitative results. The goal of this work is to develop and test improved explanation methods for quantitative decision models so that intelligent decision systems can be used in a consultative mode. We have developed a program called QxQ, which uses symbolic reasoning to provide qualitative text explanations for the results of decision trees. This work is intended to extract the key architecture- and domain-independent elements of QxQ's methodology, and to reimplement and test them as part of MIDAS, an existing IDS. First, we will develop a domain- and system-independent version of QxQ, called EQxQ (for essential QxQ), by applying it to decision models constructed in MIDAS. In collaboration with the developers of MIDAS, we will identify the key independent elements necessary to generate explanations from an IDS. Second, we will devise and implement additional explanation methods in EQxQ to allow explanation of complex modeling constructs, such as Markov models, cycle trees, and cost-effectiveness models. Third, we will test the robustness of the MIDAS-EQxQ system by using it to implement a decision model underlying a clinically relevant practice guideline. Finally, we will establish and pilot test the routine use of the program in an outpatient clinic. We will survey the physician-users to determine the strengths and weaknesses of the program. Using pilot data from the program's routine use, we will design a prospective clinical evaluation of the system and its explanations by measuring their effect on patient outcome. Our study will provide data regarding the clinical utility of intelligent decision systems.

越来越认识到，尽管实践指南可以 倾向于医生的行为改变，甚至是最受人尊敬的， 临床相关的指南通常不会影响实际实践。 尽管医生显然不愿合并已发表的 练习指南进入其临床例程，有迹象表明 立即自动提醒会对医师产生深远的影响 行为，以及医疗的成本和质量。作为实践 指南扩散并变得更加复杂，面临的困境 谁设计实践指南是权衡 准则的全面性以及它们的可能性 由他们预定的医生保留和实施。 同样的复杂性可以使基于计算机的基于计算机的实现复杂化 指南。例如，指南对患者的依赖性 偏好和个人风险因素使基于规则的方法 对于许多准则，不切实际。 智能决策系统（IDS）是专门设计的 根据不同的临床情况量身定制的复杂，灵活的指南。 这些系统根据量身定制的决策分析模型提供建议。 但是这些系统假设医师用户将被 审查决策模型及其相关的定量结果。 这项工作的目的是开发和测试改进的解释方法 对于定量决策模型，智能决策系统可以 在协商模式下使用。我们已经开发了一个名为QXQ的程序， 它使用符号推理提供定性文本解释 决策树的结果。这项工作旨在提取钥匙 QXQ方法论的架构和域与无关的元素，以及 重新进化并作为现有ID的MIDAS的一部分进行测试。首先，我们 将开发QXQ的域和系统独立的版本，称为EQXQ （对于基本QXQ），将其应用于在构建的决策模型中 米达斯。与MIDAS的开发商合作，我们将确定 从IDS生成解释所需的关键独立元素。 其次，我们将设计并实施其他解释方法 EQXQ允许解释复杂的建模构建体，例如Markov 模型，周期树和成本效益模型。第三，我们将测试 MIDAS-EQXQ系统的鲁棒性通过使用它来实现 临床相关实践指南的决策模型。 最后，我们将建立并进行试验测试程序的常规使用 在门诊诊所中。我们将调查医生用户以确定 该计划的优势和劣势。使用来自 程序的常规使用，我们将设计一个预期的临床评估 该系统及其解释通过衡量其对患者的影响 结果。我们的研究将提供有关临床实用程序的数据 智能决策系统。