INTRODUCTION TO THE SPECIAL ISSUE ON CASE-BASED REASONING IN THE HEALTH SCIENCES

There has been an explosion of interest in health sciences applications of case-based reasoning (CBR), not only in the traditional CBR in medicine domain, but also in bioinformatics, enabling home health-care technologies, CBR integration, and synergies between CBR and knowledge discovery. This special issue features the best papers from the third workshop on CBR in the health sciences, held at ICCBR-05 in Madrid. It is the third in a series of exciting workshops, the first two of which were held at ICCBR-03, in Trondheim, Norway, and at ECCBR-04, in Madrid, Spain. The nine high-quality papers introduced here represent the research and experience of twenty-two authors working in eight different countries on a wide range of problems and projects. These papers illustrate some of the major trends of current research in CBR in the health sciences, and represent overall an excellent sample of the most recent advances of CBR in the health sciences.     

PROTOTYPICAL CASES FOR RETRIEVAL, REUSE, AND KNOWLEDGE MAINTENANCE IN BIOMEDICAL CASE-BASED REASONING

Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Case-based reasoning (CBR) holds the promise to represent contextual knowledge in a way that was not possible before with traditional knowledge-based methods. One main issue in biomedical CBR is dealing with the rate of generation of new knowledge in biomedical fields, which often makes the content of a case base partially obsolete. This article proposes to make use of the concept of prototypical case to ensure that a CBR system would keep update with current research advances in the biomedical field. Prototypical cases have served various purposes in biomedical CBR systems, among which to organize and structure the memory, to guide the retrieval as well as the reuse of cases, and to serve as bootstrapping a CBR system memory when real cases are not available in sufficient quantity and/or quality. This paper emphasizes the different roles prototypical cases can play in CBR systems, and presents knowledge maintenance as a very important novel role for these prototypical cases.     

Prototypical case mining from biomedical literature for bootstrapping a case base

This article addresses the task of mining for cases from biomedical literature to automatically build an initial case base for a case-based reasoning (CBR) system. This research takes place within the Mémoire project, which has for goal to provide a framework to facilitate building CBR systems in biology and medicine. By analyzing medical literature, the ProCaseMiner system mines for medical concepts such as diseases, signs and symptoms, laboratory tests, and treatment plans in relationship with one another, and connects them together in a given medical domain. It then organizes these concepts in a higher-level structure called a case. This case mining component provides a definite help to bootstrap the creation of a biomedical CBR system case base, composed of both concrete cases and prototypical cases. Currently, most cases learnt correspond to prototypical cases, given the level of abstraction of their features. This article validates the approach by presenting a comparison between the prototypical cases learnt from stem-cell transplantation domain with those created by a team of experts in the domain.     

CASE-BASED REASONING AND KNOWLEDGE DISCOVERY IN MEDICAL APPLICATIONS WITH TIME SERIES

This paper discusses the role and integration of knowledge discovery (KD) in case-based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case-specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by a real application scenario in medicine in which time series of patterns are to be analyzed and classified. As single pattern cannot convey sufficient information in the application, sequences of patterns are more adequate. Hence it is advantageous if sequences of patterns and their co-occurrence with categories can be discovered. Evaluation with cases containing series classified into a number of categories and injected with indicator sequences shows that the approach is able to identify these key sequences. In a clinical applica-tion and a case library that is representative of the real world, these key sequences would improve the classification ability and may spawn clinical research to explain the co-occurrence between certain sequences and classes.     

信息资源开发新技术——数据挖掘

数据挖掘是用于信息资源开发的一种新的数据处理技术。本文介绍了数据挖掘的概念、特点、功能、常用方法、并与传统的数据处理方法进行比较;对数据挖掘工具的选用,提出了一些建议。     

数据挖掘的认识及其在高校就业工作中的分析

随着信息化建设的不断深入,各类数据、信息急剧增长。如何对大量数据进行深入分析和利用,并从中发现有用的知识,已成为信息化社会面临的重要问题。数据挖掘就是从大量数据中提取或"挖掘"知识,从而实现"数据一信息一知识"的过程。     

Naive—Bayes模型及其在范例推理中的应用

简单贝叶斯模型能够很好地综合先验信息和样本信息。探讨了利用简单贝叶斯模型进行范例推理的可行性,并提出了对范例进行分类和学习的算法。实验结果表明,系统的分类准确度及其学习速度都较高,该方法有效可行。     

数据挖掘的概念、系统结构和方法

首先对数据挖掘的概念及相关流派加以归纳,然后给出一个数据挖掘系统的体系结构,并通过它介绍数据挖掘系统的主要功能部件,最后对数据挖掘的主要方法进行分析。     

对数据挖掘技术的探讨

进入信息社会以来，各类数据、信息急剧地增长，具有海量、冗余的性质，这时人们开始考虑：如何才能不被信息淹没，并且能从中及时发现有用的知识，以提高信息的利用率，而这个任务就落在数据挖掘的身上。数据中蕴涵着知识．数据挖掘正是从大量数据中提取或“挖掘”知识，从而实现从“数据→信息→知识”的过程，为各信息技术领域的发展提供强大的支持。在下文中，将对数据挖掘这种技术进行讨论．     

基于数据挖掘和范例推理的智能分析决策支持技术综述

介绍了数据挖掘中的一些关键技术、人工智能基于范例推理、决策支持的主要理论及其发展,提出了范例推理、类比学习、规则推理之间的联系,详细探讨了数据挖掘技术、基于范例推理和决策支持理论集成的问题,最后对上述技术在预测领域的综合应用前景作了探讨。     

Modeling adaptation of breast cancer treatment decision protocols in the <Emphasis Type=SmallCaps>Kasimir</Emphasis> project

Medical decision protocols constitute theories for health-care decision making that are applicable for “standard” medical cases but have to be adapted for the other cases. This holds in particular for the breast cancer treatment protocol studied in the Kasimir research project. Protocol adaptations can be seen as knowledge-intensive case-based decision support processes. Some examples of adaptations that have been performed by oncologists are presented in this paper. Several issues are then identified that need to be addressed while trying to model such processes, namely: the complexity of adaptations, the lack of relevant information about the patient, the necessity to take into account the applicability and the consequences of a decision, the closeness to decision thresholds, and the necessity to consider some patients according to different viewpoints. As handling these issues requires some additional knowledge, which has to be acquired, different methods are presented that perform adaptation knowledge acquisition either from experts, or in a semi-automatic manner. A discussion and a conclusion end the paper.     

基于数据挖掘的知识发现在水电站优化调度中的应用研究

主要讨论基于数据挖掘技术的知识发现在水电调度系统中的应用,提出了基于数据挖掘的知识发现方法,建立了知识向量集的拓扑空间概念并提出了基于拓扑空间向量集的不确定性知识表示方法。     

数据挖掘管理系统

本文分析了数据挖掘的常用技术和分析方法,详细探讨了数据挖掘过程,在此基础上,设计了一个由多模型构成的数据挖掘管理系统的一般框架,其能支持数据挖掘分析的全过程。     

一种时序数据的离群数据挖掘新算法

离群数据挖掘是数据挖掘的重要内容,针对时序数据进行离群数据挖掘方法的研究。首先通过对时序数据进行离散傅立叶变换将其从时域空间变换到频域空间,将时序数据映射为多维空间的点,在此基础上,提出一种新的基于距离的离群数据挖掘算法。对某钢铁企业电力负荷时序数据进行仿真实验,结果表明了算法的有效性。     

在Windows XP中“捉迷藏”

自从把电脑搬到宿舍后，它就基本被室友们当作了“公用设施”，特别是有几个不安分的家伙总喜欢东搞西弄的，常常将Windows XP系统玩到“面目全非”。这样平时要在电脑上放点自己的东西，便没了一丝安全感，想装个加密之类的软件，又有点欲盖弥彰的感觉。实在没有办法，只好利用Windows XP中的各项设置，在系统中打了一场“暗战”！     

对数据挖掘技术的探讨

进入信息社会以来,各类数据、信息急剧地增长,具有海量、冗余的性质,这时人们开始考虑:如何才能不被信息淹没,并且能从中及时发现有用的知识,以提高信息的利用率,而这个任务就落在数据挖掘的身上。数据中蕴涵着知识,数据挖掘正是从大量数据中提取或“挖掘”知识[1],从而实现从“数据→信息→知识”的过程[2],为各信息技术领域的发展提供强大的支持。在下文中,将对数据挖掘这种技术进行讨论。     

基于事例的预报警技术在设备监控系统中的应用研究

基于事例的推理是直接利用相似的历史事件来解求当前问题的新技术,属于数据挖掘的范畴。本文介绍了数据挖掘的发展概况和应用现状。重点讨论了数据挖掘技术之一——基于事例的推理在设备监控领域中的应用。文中讨论了相关的关键技术,并给出了具体的应用实例。