Stamper R, Todd B S, Macpherson P
Programming Research Group, Oxford University Computing Laboratory, UK.
Methods Inf Med. 1994 May;33(2):205-13.
One of the most accountable methods of providing machine assistance in medical diagnosis is to retrieve and display similar previously diagnosed cases from a database. In practice, however, classifying cases according to the diagnoses of their nearest neighbours is often significantly less accurate than other statistical classifiers. In this paper the transparency of the nearest neighbours method is combined with the accuracy of another statistical method. This is achieved by using the other statistical method to define a measure of similarity between the presentations of two cases. The diagnosis of abdominal pain of suspected gynaecological origin is used as a case study to evaluate this method. Bayes' theorem, with the usual assumption of conditional independence, is used to define a metric on cases. This new metric was found to correspond as well as Hamming distance to the clinical notion of "similarity" between cases, while significantly increasing accuracy to that of the Bayes' method itself.
在医学诊断中提供机器辅助的最可靠方法之一是从数据库中检索并显示先前诊断的类似病例。然而，在实践中，根据最近邻病例的诊断对病例进行分类往往比其他统计分类器的准确性要低得多。在本文中，最近邻方法的透明度与另一种统计方法的准确性相结合。这是通过使用另一种统计方法来定义两个病例表现之间的相似性度量来实现的。以疑似妇科来源的腹痛诊断为例进行研究，以评估该方法。在通常的条件独立性假设下，使用贝叶斯定理来定义病例之间的度量。发现这种新度量与汉明距离一样，与病例之间“相似性”的临床概念相对应，同时显著提高了与贝叶斯方法本身相比的准确性。
