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一种用于医学诊断的模糊概率方法。

A fuzzy probabilistic method for medical diagnosis.

作者信息

Mak D K

机构信息

Canada Federal Government Research Laboratories, Ottawa, Canada,

出版信息

J Med Syst. 2015 Mar;39(3):26. doi: 10.1007/s10916-015-0203-9. Epub 2015 Feb 10.

Abstract

The max-min composition in fuzzy set theory has attained reasonable success in medical diagnosis in the past thirty years for estimating the probability of a patient diagnosed with a certain disease. However, there has been no theoretical justification why the method would work. We create a theoretical model to calculate the probabilities of hypothetical patients having designated diseases, and use simulated dataset to explain why the max-min composition has been successful. In addition, based on the theoretical model, we propose a fuzzy probabilistic method to estimate the probability of a patient having a certain disease. The proposed method may produce a more accurate estimate than the max-min composition.

摘要

在过去三十年里,模糊集理论中的最大-最小合成法在医学诊断中取得了一定的成功,用于估计患者被诊断患有某种疾病的概率。然而,该方法为何有效却一直缺乏理论依据。我们创建了一个理论模型来计算假设患者患有特定疾病的概率,并使用模拟数据集来解释最大-最小合成法为何取得成功。此外,基于该理论模型,我们提出了一种模糊概率方法来估计患者患有某种疾病的概率。所提出的方法可能比最大-最小合成法产生更准确的估计。

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