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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疾病诊断支持

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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)是一种从过去的经验中学习的技术。在经验在解决新问题中起重要作用的领域(例如医学诊断中的情况)中,它的使用引起了人们的极大兴趣。本文提出了一种基于CBR的决策支持系统,并将其应用于慢性阻塞性肺疾病(COPD)的诊断,这是一种与烟草有关的危险性呼吸系统疾病。在医疗活动中,医师通常处于必须做出决定而他们没有所有必要数据的情况下,那么他们实质上是根据自己的经验来找到最可能的诊断方法。我们的系统旨在通过在CBR过程的最重要阶段(包括检索最相似的病例)中估计缺少数据的属性的相似性来重现医生的这种行为。我们提出了实施和测试的三个想法,以找到对缺少数据的病例进行真正的诊断。还开发了一些启发式函数来测量具有符号性质的属性的相似性。这些想法和启发式方法的初步实验已证明对结果有很好的影响。

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