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Reasoning with graded information: The case of diagnostic rating scales in healthcare

机译：使用分级信息进行推理：医疗保健中的诊断等级量表

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摘要

In medicine one frequently deals with vague information. As a tool for reasoning in this area, fuzzy logic suggests itself. In this paper we explore the applicability of the basic ideas of fuzzy set theory in the context of medical assessment questionnaires, which are commonly used, for instance, to support the diagnosis of psychological disorders. The items of a questionnaire are answered in a graded form; patients are asked to choose an element on a linear scale. The derived diagnostic hypotheses are graded as well. This leads to the question whether there is a logical formalism that is suitable to capture the score calculation of medical assessment questionnaires and thereby provides a mathematical justification of the way in which the calculation is typically done. We elaborate two alternative approaches to this problem. First, we follow the lines of mathematical fuzzy logic. For the proposed logic, which can deal with the formation of mean values, we present a Hilbert-style deduction system. In addition, we consider a variant of the prototype approach to vagueness. In this case we are led to a framework for which to obtain a logical calculus turns out to be difficult, yet our gain is a model that is conceptually comparably well-justifiable. (c) 2015 Elsevier B.V. All rights reserved.
机译：在医学上，一个人经常处理含糊的信息。作为这方面的推理工具，模糊逻辑表明了自己。在本文中，我们探讨了模糊集理论的基本思想在医学评估问卷中的适用性，这些问卷通常用于支持心理疾病的诊断。问卷的项目以分级形式回答；要求患者选择线性比例的元素。派生的诊断假设也被分级。这就提出了一个问题，即是否存在一种逻辑形式主义，该逻辑形式主义适合于捕获医学评估调查表的分数计算，从而为通常进行计算的方式提供数学依据。我们阐述了解决此问题的两种替代方法。首先，我们遵循数学模糊逻辑的路线。对于可以处理均值形成的拟议逻辑，我们提出了希尔伯特式的推导系统。此外，我们考虑了模糊性原型方法的一种变体。在这种情况下，我们被引导到一个很难获得逻辑演算的框架，但是我们的收益是一个在概念上可比较合理的模型。 （c）2015 Elsevier B.V.保留所有权利。

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