Papageorgiou Elpiniki, Stylios Chrysostomos, Groumpos Peter
Lab. for Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras, 26500, Greece.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:1192-5. doi: 10.1109/IEMBS.2007.4352510.
Medical problems involve different types of variables and data, which have to be processed, analyzed and synthesized in order to reach a decision and/or conclude to a diagnosis. Usually, information and data set are both symbolic and numeric but most of the well-known data analysis methods deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered. The medical decision support methods usually are accessed in only one type of available data. Thus, sophisticated methods have been proposed such as integrated hybrid learning approaches to process symbolic and numeric data for the decision support tasks. Fuzzy Cognitive Maps (FCM) is an efficient modelling method, which is based on human knowledge and experience and it can handle with uncertainty and it is constructed by extracted knowledge in the form of fuzzy rules. The FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This rule base could be derived by a number of machine learning and knowledge extraction methods. Here it is introduced a hybrid attempt to handle situations with different types of available medical and/or clinical data and with difficulty to handle them for decision support tasks using soft computing techniques.
医学问题涉及不同类型的变量和数据，为了做出决策和/或得出诊断结论，必须对这些变量和数据进行处理、分析和综合。通常，信息和数据集既有符号型的，也有数值型的，但大多数知名的数据分析方法只处理一种类型的数据。即使考虑模糊方法（其不依赖于变量的尺度），通常也只考虑数值数据。医学决策支持方法通常仅针对一种可用数据进行应用。因此，已经提出了复杂的方法，如集成混合学习方法，用于处理符号型和数值型数据以完成决策支持任务。模糊认知图（FCM）是一种有效的建模方法，它基于人类知识和经验，能够处理不确定性，并且由以模糊规则形式提取的知识构建而成。如果有模糊规则库（IF - THEN规则），则可以增强FCM模型。这个规则库可以通过多种机器学习和知识提取方法推导得出。这里介绍了一种混合尝试，即使用软计算技术来处理具有不同类型可用医学和/或临床数据且难以处理这些数据以用于决策支持任务的情况。
