Fuzzy theory approach for temporal model-based diagnosis: An application to medical domains
Fuzzy theory approach for temporal model-based diagnosis: An application to medical domains
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DOI：
10.1016/j.artmed.2006.03.004
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发表时间：
2006-10-01
影响因子：
7.5
通讯作者：
Marin, Roque
中科院分区：
文献类型：
--
作者：
Palma, Jose;Juarez, Jose M.;Marin, Roque
目的：这项工作的目的是提供一个理论框架，这是足够的表达来描述时间演变的疾病，并提出了一个诊断过程，用于建立解释病人的观察到的时间演变的基础上，这些疾病的描述。背景：基于模型的诊断（MBD）解决了故障排除系统的问题，从一个描述他们的结构和功能（或行为）。正是在这一领域，作为MBD系统的一部分，深度因果模型的使用已经显示出比经典的基于规则的系统更高的效率。从一开始，时间维度就被认为是MBD的重要组成部分，因为它可以定义动态行为。已经提出了几种方法来表示在MBD的时间，使时间的概念和关系的表示，以及使用时间推理mechanism.Methodology：我们首先提出了一个时间的行为模型（TBM），它使我们能够捕捉到动态的疾病的时间演变，并包括上下文信息。需要背景信息来模拟背景因素如何改变疾病的时间演变。时间部分采用模糊时间约束网络（FTCN）建模，使定量和定性的不精确时间信息的表示成为可能。我们还提供了一个诊断过程，这是基于一个时间适应的经典的覆盖和区分methods.Results：提出的TBM和诊断过程提供了一个独特的框架，解决了三个问题没有一起处理到目前为止：（a）列入上下文信息，（B）的表达能力提供的解决方案，和（c）诊断假设的评价。这一建议表明，FTCN形式主义提供了足够的表达机制，以科普内在的不精确性疾病的时间演变的描述。生成的解释为用户提供了疾病时间演变及其因果关系的完整画面，从而允许随着时间的推移出现相同疾病的重复实例。机制提供了评估的可信度的替代假设，基于可能性理论。一个原型是沿着知识获取工具。指导医学专家在模型构建过程中。结论：在本文中，我们提出了一个模型，紧密耦合的方法从MBD领域与基于约束的时间推理技术。所提出的模型允许我们以紧凑的方式对复杂的上下文关系进行建模。因为它提供的解决方案表达能力足以用于决策支持目的。所提供的解决方案符合一个因果网络，需要异常的观察，包括病理生理和病因状态。此外，位于不同时刻的相同诊断假设的不同实例在最终解决方案中也是可能的。最后，我们提供了相关的和未来的工作分析。(C)2006 Elsevier B.V.保留所有权利。
Objective: The aim of this work is to provide a theoretical framework which is sufficiently expressive to describe temporal evolution of diseases, and also to propose a diagnostic process for building explanations of patient's observed temporal evolution based on these disease descriptions.Background: Model-based diagnosis (MBD) tackles the problem of troubleshooting systems by starting from a description of their structure and function (or behaviour). It is in this area where the use of deep causal models, as part of MBD systems, has shown its greater efficiency over classical rule based systems. From its beginnings, the temporal dimension was considered as an important component in MBD, since it makes it possible to define the dynamic behaviour. Several approaches have been proposed to represent time in MBD, enabling the representation of temporal concepts and relations, as well as the use of temporal reasoning mechanisms.Methodology: We first propose a temporal behavioural model (TBM), which allows us to capture the dynamics underlying temporal evolution of diseases and to include contextual information. Contextual information is required to model how contextual factors change the temporal evolution of diseases. The temporal component is modelled by fuzzy temporal constraints networks (FTCN), which makes the representation of quantitative and qualitative imprecise temporal information possible. We also provide a diagnostic process, which is based on a temporal adaptation of classical cover and differentiate method.Results: The TBM and diagnostic process proposed provides a unique framework which addresses three problems not dealt with together so far: (a) the inclusion of contextual information, (b) the expressivity of the solution provided, and (c) the evaluation of the diagnostic hypotheses. This proposal demonstrates that the FTCN formalism provides mechanisms sufficiently expressive to cope with the intrinsic imprecision in the description of diseases' temporal evolution. The explanation generated provides the user with a complete picture of the temporal evolution of diseases and its causal links, thus allowing the appearance of repeated instances of the same disease through time. Mechanisms are provided which evaluate the credibility of alternative hypotheses, based on possibility theory. A prototype is presented along with a knowledge acquisition tool. that guides medical experts in the model building process.Conclusions: In this paper, we propose a model that tightly couples methods from MBD area with constraint-based temporal reasoning techniques. The proposed model allows us to model complex contextual relationships in a compact way as well. as providing solutions expressive enough to be used for decision support purposes. The solution provided conforms a causal network entailing the abnormal observations, including pathophysiological and etiological states. Furthermore, different instances of the same diagnostic hypotheses, located at different time instants, are also possible in the final solution. Finally, we provide an analysis of related and future works. (C) 2006 Elsevier B.V. All rights reserved.