Abstract

This paper discusses the possibility of expanding the ideas of the validity of medical decisions of a diagnostic nature, which are made in the framework of so-called evidence-based medicine. An approach is proposed that allows building special data in the process of intelligent analysis of accumulated empirical data, which characterize the causality of a diagnosed effect–logical conditions (characteristic functions) that take the value true in all instances of the presence of the target effect and the value false for all instances of its absence in the training sample of precedents. This problem is solved based on the expanding sequences of training samples using: (a) a formal refinement of the concept of similarity of precedent descriptions as a binary algebraic operation, and (b) a mathematical technique for generating empirical dependences in the style of the JSM method of automated support for scientific research. The features and capabilities of the developed approach are described based on the example of solving the problem of analyzing the causes and predicting the pseudoprogression of brain tumors.

中文翻译：

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关于医学诊断证据的问题：有限大小样本中患者经验数据的智能分析

摘要

本文讨论了在所谓的循证医学框架内扩展诊断性质的医学决策有效性概念的可能性。提出了一种方法，该方法允许在对累积的经验数据进行智能分析的过程中构建特殊数据，该方法可表征诊断出的效果的因果关系–逻辑条件（特征函数）在目标效果存在的所有情况下均为真对于在先例训练样本中不存在的所有实例，该值为false。通过使用以下方法扩展训练样本的序列来解决此问题：（a）将先验描述的相似性概念作为二进制代数运算进行形式上的改进，（b）一种数学技术，用于以自动支持科学研究的JSM方法的方式生成经验依赖性。以解决问题的原因分析和预测脑瘤假性进展为例，描述了该方法的特点和功能。