Lavrac N
Department of Intelligent Systems, J. Stefan Institute, Ljubljana, Slovenia.
Artif Intell Med. 1999 May;16(1):3-23. doi: 10.1016/s0933-3657(98)00062-1.
Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis to be coupled with methods for efficient computer-assisted analysis. This paper presents selected data mining techniques that can be applied in medicine, and in particular some machine learning techniques including the mechanisms that make them better suited for the analysis of medical databases (derivation of symbolic rules, use of background knowledge, sensitivity and specificity of induced descriptions). The importance of the interpretability of results of data analysis is discussed and illustrated on selected medical applications.
医学信息系统的广泛使用和医学数据库的爆炸式增长要求传统的手动数据分析与高效的计算机辅助分析方法相结合。本文介绍了可应用于医学的特定数据挖掘技术，特别是一些机器学习技术，包括使它们更适合医学数据库分析的机制（符号规则的推导、背景知识的使用、归纳描述的敏感性和特异性）。文中讨论了数据分析结果可解释性的重要性，并通过选定的医学应用进行了说明。
