Data mining on temporal data: a visual approach and its clinical application to hemodialysis

The quantity and complexity of data acquired, time-stamped and stored in clinical databases by automated medical devices is rapidly and continuously increasing. As a result, it becomes more and more important to provide clinicians with easy-to-use interactive tools to analyze huge amounts of this data. This paper proposes an approach for visual data mining on temporal data and applies it to a real medical problem, i.e. the management of hemodialysis. The approach is based on the integration of 3D and 2D information visualization techniques and offers a set of interactive functionalities that will be described in detail in the paper. We will also discuss how the system has been evaluated with end users and how the evaluation led to changes in system design.     

医学数据挖掘系统研究——糖尿病并发症流行病学知识发现

针对流行病学研究的特点,论文提出计算机辅助医学数据挖掘系统构架,以糖尿病并发症为研究实例,探讨医学数据的冗余性消除、规范化储存、知识归纳及可视化表达等问题。以天津总医院3022例普查数据为研究对象,尝试解决用计算机实现糖尿病并发症这类定性数据的定量化数据挖掘和知识发现。通过对于43种并发症的定性数据挖掘,可以发现诸如高血脂、冠心病、高血压、脑血管病等具有明显并发倾向的知识规则18条。同时,采用知识树方式和决策树等方法实现知识规则的可视化表达。基于数据挖掘和知识发现计算机辅助医学数据挖掘系统能够对现有病历数据库中数据进行自动分析并且提供有价值医学知识,特别适合流行病学分析和全民健康评估,因此与社区医疗和医院HIS系统结合是未来一个非常现实的发展方向。     

Knowledge discovery in medicine: Current issue and future trend

Data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge extraction. Data mining is supposed to be able to handle various data types in all formats. Relevance of this paper is emphasized by the fact that data mining is an object of research in different areas. In this paper, we review previous works in the context of knowledge extraction from medical data. The main idea in this paper is to describe key papers and provide some guidelines to help medical practitioners. Medical data mining is a multidisciplinary field with contribution of medicine and data mining. Due to this fact, previous works should be classified to cover all users’ requirements from various fields. Because of this, we have studied papers with the aim of extracting knowledge from structural medical data published between 1999 and 2013. We clarify medical data mining and its main goals. Therefore, each paper is studied based on the six medical tasks: screening, diagnosis, treatment, prognosis, monitoring and management. In each task, five data mining approaches are considered: classification, regression, clustering, association and hybrid. At the end of each task, a brief summarization and discussion are stated. A standard framework according to CRISP-DM is additionally adapted to manage all activities. As a discussion, current issue and future trend are mentioned. The amount of the works published in this scope is substantial and it is impossible to discuss all of them on a single work. We hope this paper will make it possible to explore previous works and identify interesting areas for future research.     

应用稀疏数组存储病案首页数据研究

病历数据性质特殊，一般数据模型用于其管理比较困难，因此需要研究寻找适合的特殊数据模型。病案首页是病历的一种摘要，病历数据的许多特性均反映到病案首页中。本文将介绍一种应用稀疏数组存储病案首页的存储结构设计，以及基于稀疏数组的病案首页系统如何利用稀疏数组的特性，使存储结构既能保证长久数据的应用连续性，又能不断适应结构变化，同时介绍其独特的数据存储体系和数据备份方案。     

Data Mining: How Research Meets Practical Development?

At the 2001 IEEE International Conference on Data Mining in San Jose, California, on November 29 to December 2, 2001, there was a panel discussion on how data mining research meets practical development. One of the motivations for organizing the panel discussion was to provide useful advice for industrial people to explore their directions in data mining development. Based on the panel discussion, this paper presents the views and arguments from the panel members, the Conference Chair and the Program Committee Co-Chairs. These people as a group have both academic and industrial experiences in different data mining related areas such as databases, machine learning, and neural networks. We will answer questions such as (1) how far data mining is from practical development, (2) how data mining research differs from practical development, and (3) what are the most promising areas in data mining for practical development.     

基于数据铸造新技术的肺癌生存率分析系统的设计与应用

生物信息学作为21世纪最有影响力的一个领域,已经取得了前所未有的进步。数据库是其成功发展的一个关键因素。然而,如何将多个数据源集成为一个单一,一致的数据仓库,并在其上建构多种解决某领域专业知识的强大数据挖掘方法,依然是个难题。文章采用一种基于数据铸造机制的数据仓库新技术,提出专门用于预测肺癌生存期的决策算法,进行肺癌生存率的分析,从而有效地采用新型数据挖掘系统解决肺癌生存率预测。     

聚类与关联规则在信息舞弊识别中的应用

针对现代电子数据迅速膨胀,传统的审计方式已经无法应对海量的业务数据,试图将数据挖掘中的聚类和关联规则算法引入审计领域．在研究聚类与关联规则算法的含义及相关算法—K-Means和Apriori算法的基础上,提出了一种基于聚类与关联规则的审计模型,并以某市城镇医疗保险的审计为例,首先利用聚类分析进行数据筛选,然后利用关联规则挖掘海量数据之间潜在的关系,为审计提供线索．文章通过案例分析为数据挖掘在信息舞弊识别领域的应用提供参考．     

A Survey of Uncertain Data Algorithms and Applications

In recent years, a number of indirect data collection methodologies have lead to the proliferation of uncertain data. Such data points are often represented in the form of a probabilistic function, since the corresponding deterministic value is not known. This increases the challenge of mining and managing uncertain data, since the precise behavior of the underlying data is no longer known. In this paper, we provide a survey of uncertain data mining and management applications. In the field of uncertain data management, we will examine traditional methods such as join processing, query processing, selectivity estimation, OLAP queries, and indexing. In the field of uncertain data mining, we will examine traditional mining problems such as classification and clustering. We will also examine a general transform based technique for mining uncertain data. We discuss the models for uncertain data, and how they can be leveraged in a variety of applications. We discuss different methodologies to process and mine uncertain data in a variety of forms.     

数据整合与数据挖掘技术在医疗保险信息系统的研究与应用

通过分析医疗保险管理信息化深入发展的需求,从技术的角度提出医疗保险信息系统数据整合及数据挖掘的总体解决方案,并对医疗保险信息系统的数据仓库的设计、数据整合的方案以及数据挖掘的技术和应用进行概要的分析和论述,并用关联规则挖掘算法实证研究医保信息挖掘的可能性与必要性。利用编码、解码技术和SQL的聚集函数,实现基于SQL的FP-Growth算法,从而突破机器内存对数据挖掘的处理效率,实现对海量数据挖掘的高效挖掘。     

A service composition approach based on sequence mining for migrating e-learning legacy system to SOA

With the fast development of business logic and information technology, today's best solutions are tomorrow's legacy systems. In China, the situation in the education domain follows the same path. Currently, there exists a number of e-learning legacy assets with accumulated practical business experience, such as program resource, usage behaviour data resource, and so on. In order to use these legacy assets adequately and efficiently, we should not only utilize the explicit assets but also discover the hidden assets. The usage behaviour data resource is the set of practical operation sequences requested by all users. The hidden patterns in this data resource will provide users' practical experiences, which can benefit the service composition in service-oriented architecture (SOA) migration. Namely, these discovered patterns will be the candidate composite services (coarse-grained) in SOA systems. Although data mining techniques have been used for software engineering tasks, little is known about how they can be used for service composition of migrating an e-learning legacy system (MELS) to SOA. In this paper, we propose a service composition approach based on sequence mining techniques for MELS. Composite services found by this approach will be the complementation of business logic analysis results of MELS. The core of this approach is to develop an appropriate sequence mining algorithm for mining related data collected from an e-learning legacy system. According to the features of execution trace data on usage behaviour from this e-learning legacy system and needs of further pattern analysis, we propose a sequential mining algorithm to mine this kind of data of the legacy system. For validation, this approach has been applied to the corresponding real data, which was collected from the e-learning legacy system; meanwhile, some investigation questionnaires were set up to collect satisfaction data. The investigation result is 90% the same with the result obtained through our approach.     

基于前缀集约束的临床路径挖掘算法

大量的研究表明,临床路径在提高医院运行效率上发挥了极大的作用,但是怎样方便快捷地找到某种疾病的临床路径是一个关键的问题.随着信息技术的发展,数据存储能力以及数据收集能力的提高,各大中型医院都积累了大量的临床诊疗数据,这为数据挖掘技术应用到临床路径发现提供了基础.在这篇文章中,我们把临床路径挖掘问题抽象成频繁序列模式挖掘问题,我们首次提出了临床路径前缀集的概念,并在此基础上提出了基于前缀集的临床路径挖掘算法CPM-PC （Clinical Pathways Mining with Prefix Constraints）,这个算法更适用于临床路径挖掘,挖掘出的序列模式有更强的医学意义,这个算法已经被应用到一个真实的数据集上并且取得良好的效果.     

基于数据立方体的数据挖掘系统

介绍了一个通用的数据挖掘系统-基于数据立方体的数据挖掘系统的设计与实现过程。该系统基于C/S构架，引入了挖掘模型的概念，集成了两种算法，图形化显示挖掘结果。文中详细介绍了系统的这些特点。     

Privacy-preserving models for comparing survival curves using the logrank test

The incorporation of electronic health care in medical institutions will benefit and thus further boost the collaborations in medical research among clinics and research institutions. However, privacy regulations and security concerns make such collaborations very restricted. In this paper, we propose privacy preserving models for survival curves comparison based on logrank test, in order to perform better survival analysis through the collaboration of multiple medical institutions and protect the data privacy. We distinguish two collaboration scenarios and for each scenario we present a privacy preserving model for logrank test. We conduct experiments on the real medical data to evaluate the effectiveness of our proposed models.     

Data quality aspects of a database for abdominal septic shock patients

Since many years, medical researchers have investigated the mechanisms that may cause a septic shock. Despite many approaches that analyzed smaller parts of the relevant data or single variables, respectively, no larger database with all the possible relevant data existed. Our work was to bridge this gap. We built a large database for abdominal septic shock patients. While building it, we were confronted with many problems concerning the database realization and the data quality. Thus, we will demonstrate how we built our database and how we assured data quality. This is of interest for all medical or computer scientists who are concerned with building medical databases with retrospective data, e.g. for data mining purposes.     

基于全文索引与余弦公式医学文本相似性分析

医学文本相似性问题是医学文本挖掘中的重要内容,如何能够快速计算出大数据量下的医学文本的相似性情况是医学文本相似性计算的重点.针对基于传统余弦公式医学文本相似性分析算法在性能上的缺陷,提出了一种基于全文索引技术与余弦公式医学文本相似性分析算法,对医学文本相似性进行分析.采用全文索引技术对医学文本数据相关关键词进行索引,并根据若干关键词在索引中检索出部分数据,从而减少计算复杂度,提高效率.实验表明,该方法比基于传统余弦公式医学文本相似性分析算法具有更优的性能.     

基于属性的聚类算法在医生医疗质量评价系统中的应用研究

从数据挖掘的概念入手, 以数据结构的角度看待数据挖掘的研究对象, 对数据挖掘的重要工具——聚类做了深入的论述, 把聚类分为基于数据元素的Q 型聚类和基于属性的R 型聚类, 着重讨论了R 型聚类, 论述了相关的概念、技术和算法。最后介绍了一个实际应用系统———医生医疗质量评价系统, 提出了一些新的观点及算法设计思路。     

挖掘数据中实时数据预处理技术的应用

流程工业中数据挖掘结果,是以所需参数的完整性和准确性为前提的。本文综合流程工业实际情况,设计出一种基于支持向量机的实时数据预处理混合算法,通过该预处理算法,可以处理因测量仪表故障导致的数据缺失和外部干扰引起的实时数据采集误差,提高进入数据挖掘系统的数据的质量,并可对仪表故障实时诊断与报警,同时给出该实时数据预处理算法的实施步骤。基于该预处理算法设计开发的实时数据预处理系统已成功应用于某大型化工企业,获得了满意的效果。     

Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support

Spatial data mining algorithms heavily depend on the efficient processing of neighborhood relations since the neighbors of many objects have to be investigated in a single run of a typical algorithm. Therefore, providing general concepts for neighborhood relations as well as an efficient implementation of these concepts will allow a tight integration of spatial data mining algorithms with a spatial database management system. This will speed up both, the development and the execution of spatial data mining algorithms. In this paper, we define neighborhood graphs and paths and a small set of database primitives for their manipulation. We show that typical spatial data mining algorithms are well supported by the proposed basic operations. For finding significant spatial patterns, only certain classes of paths “leading away” from a starting object are relevant. We discuss filters allowing only such neighborhood paths which will significantly reduce the search space for spatial data mining algorithms. Furthermore, we introduce neighborhood indices to speed up the processing of our database primitives. We implemented the database primitives on top of a commercial spatial database management system. The effectiveness and efficiency of the proposed approach was evaluated by using an analytical cost model and an extensive experimental study on a geographic database.     

空间数据挖掘及其与智能系统的集成框架

空间数据挖掘是指从空间数据库中抽取隐含的知识、空间关系和非显式地存储在空间数据库 中有意义的特征或模式.它在遥感、地理信息系统、医疗影像、信息融合系统等领域具有广 阔的应用前景,因此日渐受到关注和重视.本文从知识发现、认知科学与智能系统交叉结合的 角度,提出了基于数据库和知识库双库协同机制的空间数据挖掘模型,并系统地介绍了从空间 数据库中可发现的知识类型及挖掘方法,然后提出了基于空间数据挖掘的新型智能系统总体 框架和系统开发基本原则,最后探讨了空间数据挖掘的发展方向.     

基于OLAP和聚类分析结合的美国专利挖掘系统

以美国授权专利数据库为实例,对OLAP及聚类分析技术进行了深入而细致的探讨。针对它们的共通性和差异性,提出了两者结合的美国专利挖掘系统的设计与实现方案,并给出了可视化结果。在此基础上,构建了数据挖掘系统的通用框架。结果表明,将OLAP和数据深层挖掘技术紧密配合、协调使用将是数据挖掘发展的一个方向和趋势。