COMPUTER-BASED EXPLANATION METHODS FOR DECISION MODELS

越来越多的人认识到，虽然实践指南可以 使医生倾向于改变行为，即使是最受尊敬的， 临床相关指南通常不影响实际实践。 尽管医生们显然不愿意将已发表的 实践指南纳入其临床常规，有迹象表明， 即时自动提醒可以对医生产生深远影响， 行为，以及医疗保健的成本和质量。随着实践 准则激增，变得更加复杂，面临的困境， 谁设计实践指南是权衡之间的 指导方针的全面性及其可能性 由其预期使用的医生保留和实施。 这种相同的复杂性可以使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得基于规则的方法 对于许多准则来说是不切实际的。 智能决策系统（IDS）是专门为实现 复杂、灵活的指南，适合不同的临床情况。 这些系统根据定制的决策分析模型提供建议。 但是这些系统假设医生用户会被一个 审查决策模型及其相关的定量结果。 这项工作的目标是开发和测试改进的解释方法 定量决策模型，使智能决策系统可以 以协商方式使用。我们开发了一个叫做QxQ的程序， 它使用符号推理来提供定性的文本解释， 决策树的结果。这项工作的目的是提取关键 QxQ方法论中与架构和领域无关的元素， 重新实现并测试它们作为MIDAS的一部分，现有的IDS。一是 将开发一个独立于领域和系统的QxQ版本，称为EQxQ (for基本QxQ），通过将其应用于 迈达斯。与MIDAS的开发人员合作，我们将确定 从IDS生成解释所需的关键独立元素。 第二，我们将设计和实施额外的解释方法， EQxQ允许解释复杂的建模结构，如马尔可夫 模型、循环树和成本效益模型。第三，我们将测试 MIDAS-EQxQ系统的鲁棒性，通过使用它来实现 决策模型的基础上临床相关的实践指南。 最后，我们将建立和试点测试程序的日常使用 在一家门诊诊所。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，我们将设计一个前瞻性的临床评价， 系统及其解释，通过测量它们对患者的影响， 结果。我们的研究将提供有关临床效用的数据， 智能决策系统

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.