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Case-based support for the diagnosis of Chronic Obstructive Pulmonary Disease

机译:基于案例的慢性阻塞性肺病的诊断支持

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Case-Based Reasoning (CBR) is a technique which consists of learning from past experiences. Its use is very interest in domains where experience plays an important role in the resolution of new problems, which is the case in medical diagnosis. This paper presents a decision making support system based on CBR and applied to the diagnosis of Chronic Obstructive Pulmonary Disease (COPD), a dangerous respiratory disease bound to tobacco. In medical activity, the physicians are often in situations where they have to make decision whereas they have not all necessary data then they are essentially based on their experiences to find the most probable diagnosis. Our system aims to reproduce this behavior of physicians by estimating similarity on attributes with missing data in the most important stage of CBR process consisting to retrieve the most similar case. We have proposed implemented and tested three ideas to find the real diagnosis of cases which have missing data. Some heuristics functions have been also developed for measuring similarity on attributes with symbolic nature. Preliminary experimentations of these ideas and heuristics have proved a good impact on results.
机译:基于案例的推理(CBR)是一种从过去经历中学习的技术。它的使用非常令人兴趣,其中经验在解决新问题方面发挥着重要作用,这是医学诊断的情况。本文介绍了基于CBR的决策支持系统,并应用于慢性阻塞性肺病(COPD)的诊断,疾病患有烟草的危险呼吸道疾病。在医学活动中,医生通常在他们必须做出决定的情况下,而他们并非所有必要的数据,那么它们基本上基于他们找到最可能诊断的经验。我们的系统旨在通过在CBR进程的最重要阶段的缺失数据中估计具有缺失数据的属性的相似性来重现这种行为,该方法包括以检索最类似的情况。我们提出实施并测试了三种想法,以找到缺失数据的案件的真实诊断。一些启发式功能也用于测量具有象征性质的属性的相似性。这些想法和启发式的初步实验已经证明了对结果的良好影响。

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