浅析基于XML的电子病历实现技术

随着医院信息化的推进,目前电子病历的研究成为医疗信息领域关注的重点。关键在于运用一种开放的、标准的、易于扩展、传输的技术实现电子病历在临床系统的应用。本文旨在通过分析 XML 技术的特点及优势论述了基于 XML 的电子病历系统的功能,进一步阐述基于如何用 XML 技术实现电手病历的结构存储、集成、信息处理以及安全问题方法。     

电子病历在复旦大学附属肿瘤医院的应用

肿瘤医院根据临床和科研发展的需要,提出了电子病历的应用需求,通过对电子病历的功能设计,基本上实现了病历文档的结构化,并在门诊和住院部各科室的应用中取得的初步成效。最后,本文对电子病历在国内医院的应用前景进行了展望。     

基于EMR的乳腺肿瘤知识图谱构建研究

知识图谱作为一种描述实体及其联系的新方法,在医学领域也逐渐得到关注,出现了多种医学知识图谱。但是这些医学知识图谱的知识大多来源于公开的医学文献,较少涉及到EMR电子病历。EMR电子病历涵盖了医院各科室各病种的患者诊疗全过程数据,具有丰富的医疗事实知识,是医学知识图谱的重要知识来源。为此,以乳腺肿瘤这一具体病种为应用实例,结合知识图谱技术的基本原理,给出了乳腺肿瘤知识图谱的定义;结合上海交通大学医学院附属瑞金医院的实际EMR电子病历数据集,通过知识抽取技术从EMR中提取乳腺肿瘤医疗事实知识。在此基础上提出乳腺肿瘤知识图谱的构建方法。     

基于电子病历的医疗质量管理平台系统研究

随着我国医疗水平的不断提高,医院之间的竞争越来越大。医院要想提升自身在市场中的竞争力,需不断提高先进技术的应用水平,保证医疗质量。文章基于电子病历研究医疗质量管理平台系统的构建,包括数据中心、数字订阅系统、任务驱动系统、学习评价系统以及用户服务系统,分析电子病历医疗质量管理平台系统的构建方法,充分发挥电子病历医疗质量管理平台系统在实际治疗中的作用,为我国医疗发展提供支持。     

基于虚拟服务的电子病历系统研究

本文针对种类与格式繁杂、数据结构多样、既有应用系统广泛的异构特性等在医疗信息数字化时存在的问题,提出了以“虚拟服务”的电子病历系统VEPRS。VEPRS围绕医疗业务系统构建,能在不影响各业务系统的前提下,隐式完成系统问的数据集成和信息共享。应用结果表明,VEPRS能够有效提高医院信息资源的流动性和利用率,为实现数字化医院的长远目标提供有力支持。     

电子病历使用中存在的问题及对策研析讨论

近年来,随着科学技术的飞速发展,医院借助于现代化的先进理念与技术提升了自身医疗服务质量和水平,以此有效增强了医院在市场中的竞争力,使医院获得了较高的经济效益、社会效益。基于此,概述了医院使用电子病历的现状,并结合具体问题提出了几方面提高医院电子病历应用质量的举措,以此为医院开展高质量、高效率电子病历应用工作提供参考经验。     

基于区块链的电子病历安全存储系统设计

区块链技术在医院电子病历安全存储系统中的应用,可以解决传统电子病历数据存储的安全问题,更好地保护电子病历数据的私密性、安全性和完整性,并实现医院电子病历数据的安全共享。对系统设计需求进行了分析,结合医院对电子病历安全存储的实际需要,把区块链技术应用到电子病历安全管理中,结合IPFS分布式存储技术构建电子病历安全系统,保证患者电子病历数据的安全,提高医院就诊服务的效率。     

HIS的惊险一“换”

对于复旦大学附属的中山医院来说,2004年有一件大事值得铭记,那就是医院新建的门诊大楼投入使用。设计美观、气派、大方的新楼使之成为上海目前就诊环境最好的医院之一。精湛的医疗技术加上赏心悦目的环境,自然吸引了不少上海乃至全国各地的病人。与崭新的门诊楼、精湛的医疗技术等硬件相比,中山医院2004年在信息化建设上也实现了一个飞跃,那就是自主开发的HIS(医院信息管理系统)正式上线运行—对计算机网络中心主任张卫国和他的同事们来说,这同样值得铭记在册。因为,信息中心在保证医院所有业务正常运转的前提下,成功替换以前系统供应商开发的HIS,实现数据全部迁移。这意味着中山医院有了自己独立的HIS。     

计算机网络开发中的电子病历系统分析

随着计算机网络的不断开发,电子病历系统已经逐步取代了传统了纸质病历,并提供了信息的电子储存、查询、统计、交换等功能,它是计算机网络应用于医疗领域的必然产物,也是医院实现计算机网络管理的必然趋势。电子病历系统在医疗机构的应用,不但提高了医院的服务质量,也大大降低了人力物力的使用。     

浅谈数字认证技术在电子病历系统中的应用

电子病历,是近今年新生的产物,随着现代信息技术的发展,在医疗领域的应用,电子病历的出现大大提高了医务工作人员的工作效率,提升了医院管理水平,对医院的管理工作有着深远的影响.然而,在实施过程中,电子病历的真实性和安全性还应有效发挥数字认证技术的作用.本文通过分析电子病历系统应用中的安全漏洞问题,针对如何高质量的发挥数字认证技术在电子病历系统中的应用进行探讨,以期通过本文的阐述有效提升电子病历系统的安全性,加快医院信息化进程.     

企业服务总线在医疗领域的实际应用

＂信息孤岛＂是医院在信息化发展中遇到的问题之一,现有的模式难以满足灵活性、适应性、可重用性、可扩展性等系统集成需求。为了更有效地管理和重用系统服务,企业服务总线（ESB）被引入到医疗信息化实践中,ESB在SOA中充当服务间智能化的集成与管理中介。以复旦大学附属肿瘤医院建设ESB的过程为例,介绍了ESB在医疗行业中应用的方法。它使用BizTalk Server ESB Toolkit作为ESB框架,并以SOA架构和医疗行业标准为基础,将医院的业务流程、应用系统和相关数据整合起来,最终以服务的方式,向外部提供统一的访问总线。总结了ESB建设过程中的关键点,以及ESB对医院信息化建设的意义。     

Tracking clusters in evolving data streams over sliding windows

Mining data streams poses great challenges due to the limited memory availability and real-time query response requirement. Clustering an evolving data stream is especially interesting because it captures not only the changing distribution of clusters but also the evolving behaviors of individual clusters. In this paper, we present a novel method for tracking the evolution of clusters over sliding windows. In our SWClustering algorithm, we combine the exponential histogram with the temporal cluster features, propose a novel data structure, the Exponential Histogram of Cluster Features (EHCF). The exponential histogram is used to handle the in-cluster evolution, and the temporal cluster features represent the change of the cluster distribution. Our approach has several advantages over existing methods: (1) the quality of the clusters is improved because the EHCF captures the distribution of recent records precisely; (2) compared with previous methods, the mechanism employed to adaptively maintain the in-cluster synopsis can track the cluster evolution better, while consuming much less memory; (3) the EHCF provides a flexible framework for analyzing the cluster evolution and tracking a specific cluster efficiently without interfering with other clusters, thus reducing the consumption of computing resources for data stream clustering. Both the theoretical analysis and extensive experiments show the effectiveness and efficiency of the proposed method. Aoying Zhou is currently a Professor in Computer Science at Fudan University, Shanghai, P.R. China. He won his Bachelor and Master degrees in Computer Science from Sichuan University in Chengdu, Sichuan, P.R. China in 1985 and 1988, respectively, and Ph.D. degree from Fudan University in 1993. He served as the member or chair of program committee for many international conferences such as WWW, SIGMOD, VLDB, EDBT, ICDCS, ER, DASFAA, PAKDD, WAIM, and etc. His papers have been published in ACM SIGMOD, VLDB, ICDE, and several other international journals. His research interests include Data mining and knowledge discovery, XML data management, Web mining and searching, data stream analysis and processing, peer-to-peer computing. Feng Cao is currently an R&D engineer in IBM China Research Laboratories. He received a B.E. degree from Xi'an Jiao Tong University, Xi'an, P.R. China, in 2000 and an M.E. degree from Huazhong University of Science and Technology, Wuhan, P.R. China, in 2003. From October 2004 to March 2005, he worked in Fudan-NUS Competency Center for Peer-to-Peer Computing, Singapore. In 2006, he received his Ph.D. degree from Fudan University, Shanghai, P.R. China. His current research interests include data mining and data stream. Weining Qian is currently an Assistant Professor in computer science at Fudan University, Shanghai, P.R. China. He received his M.S. and Ph.D. degree in computer science from Fudan University in 2001 and 2004, respectively. He is supported by Shanghai Rising-Star Program under Grant No. 04QMX1404 and National Natural Science Foundation of China (NSFC) under Grant No. 60673134. He served as the program committee member of several international conferences, including DASFAA 2006, 2007 and 2008, APWeb/WAIM 2007, INFOSCALE 2007, and ECDM 2007. His papers have been published in ICDE, SIAM DM, and CIKM. His research interests include data stream query processing and mining, and large-scale distributed computing for database applications. Cheqing Jin is currently an Assistant Professor in Computer Science at East China University of Science and Technology. He received his Bachelor and Master degrees in Computer Science from Zhejiang University in Hangzhou, P.R. China in 1999 and 2002, respectively, and the Ph.D. degree from Fudan University, Shanghai, P.R. China. He worked as a Research Assistant at E-business Technology Institute, the Hong Kong University from December 2003 to May 2004. His current research interests include data mining and data stream.     

Extracting frequent connected subgraphs from large graph sets

Mining frequent patterns from datasets is one of the key success of data mining research. Currently,most of the studies focus on the data sets in which the elements are independent, such as the items in the marketing basket. However, the objects in the real world often have close relationship with each other. How to extract frequent patterns from these relations is the objective of this paper. The authors use graphs to model the relations, and select a simple type for analysis. Combining the graph theory and algorithms to generate frequent patterns, a new algorithm called Topology, which can mine these graphs efficiently, has been proposed.The performance of the algorithm is evaluated by doing experiments with synthetic datasets and real data. The experimental results show that Topology can do the job well. At the end of this paper, the potential improvement is mentioned.     

命名和征服(英文)

This paper introduces a technique that has been used successfully in University College Dublin,Ireland and Fudan University in Shanghai,China,to teach the formal aspects of computer programming to undergraduate students.The technique,name and conquer,relies on the use of modelling small mathematical domains and allowing the results of these models to form the basis for the programming task.     

Finding centric local outliers in categorical/numerical spaces

Outlier detection techniques are widely used in many applications such as credit-card fraud detection, monitoring criminal activities in electronic commerce, etc. These applications attempt to identify outliers as noises, exceptions, or objects around the border. The existing density-based local outlier detection assigns the degree to which an object is an outlier in a numerical space. In this paper, we propose a novel mutual-reinforcement-based local outlier detection approach. Instead of detecting local outliers as noise, we attempt to identify local outliers in the center, where they are similar to some clusters of objects on one hand, and are unique on the other. Our technique can be used for bank investment to identify a unique body, similar to many good competitors, in which to invest. We attempt to detect local outliers in categorical, ordinal as well as numerical data. In categorical data, the challenge is that there are many similar but different ways to specify relationships among the data items. Our mutual-reinforcement-based approach is stable, with similar but different user-defined relationships. Our technique can reduce the burden for users to determine the relationships among data items, and find the explanations why the outliers are found. We conducted extensive experimental studies using real datasets. Jeffrey Xu Yu received his B.E., M.E. and Ph.D. in computer science, from the University of Tsukuba, Japan, in 1985, 1987 and 1990, respectively. Jeffrey Xu Yu was a research fellow in the Institute of Information Sciences and Electronics, University of Tsukuba (Apr. 1990–Mar. 1991), and held teaching positions in the Institute of Information Sciences and Electronics, University of Tsukuba (Apr. 1991–July 1992) and in the Department of Computer Science, Australian National University (July 1992–June 2000). Currently he is an Associate Professor in the Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong. His major research interests include data mining, data stream mining/processing, XML query processing and optimization, data warehouse, on-line analytical processing, and design and implementation of database management systems. Weining Qian is currently an assistant professor of computer science at Fudan University, Shanghai, China. He received his M.S. and Ph.D. degrees in computer science from Fudan University in 2001 and 2004, respectively. He was supported by a Microsoft Research Fellowship when he was doing the research presented in this paper, and he is supported by the Shanghai Rising Star Program. His research interests include data mining for very large databases, data stream query processing and mining and peer-to-peer computing. Hongjun Lu received his B.Sc. from Tsinghua University, China, and M.Sc. and Ph.D. from the Department of Computer Science, University of Wisconsin–Madison. He worked as an engineer in the Chinese Academy of Space Technology, and a principal research scientist in the Computer Science Center of Honeywell Inc., Minnesota, USA (1985–1987), and a professor at the School of Computing of the National University of Singapore (1987–2000), and is a full professor of the Hong Kong University of Science and Technology. His research interests are in data/knowledge-base management systems with an emphasis on query processing and optimization, physical database design, and database performance. Hongjun Lu is currently a trustee of the VLDB Endowment, an associate editor of the IEEE Transactions on Knowledge and Data Engineering (TKDE), and a member of the review board of the Journal of Database Management. He served as a member of the ACM SIGMOD Advisory Board in 1998–2002. Aoying Zhou born in 1965, is currently a professor of computer science at Fudan University, Shanghai, China. He won his Bachelor degree and Master degree in Computer Science from Sichuan University in Chengdu, Sichuan, China in 1985 and 1988. respectively, and a Ph.D. degree from Fudan University in 1993. He has served as a member or chair of the program committees for many international conferences such as VLDB, ER, DASFAA, WAIM, and etc. His papers have been published in ACM SIGMOD, VLDB, ICDE and some international journals. His research interests include data mining and knowledge discovery, XML data management, web query and searching, data stream analysis and processing and peer-to-peer computing.     

ARMiner: A Data Mining Tool Based on Association Rules

In this paper,ARM iner,a data mining tool based on association rules,is introduced.Beginning with the system architecture,the characteristics and functions are discussed in details,including data transfer,concept hierarchy generalization,mining rules with negative items and the re-development of the system.An example of the tool‘s application is also shown.Finally,Some issues for future research are presented.     

Algebraic specification and proof of a distributed recovery algorithm

An algebraic specification is given of an algorithm for recovery from catastrophe by a deterministic process. A second version of the algorithm also includes check-points. The algorithms are formulated in the notations of Communicating Sequential Processes (Hoare 1985) and the proofs of correctness are conducted wholly by application of algebraic laws (together with the unique fixed point theorem). He Jifeng received the B.S. degree in mathematics from Fudan University, Shanghai (China), in 1965. Then he taught in the department of mathematics, Shanghai Normal University. In 1972 he moved to East China Normal University where he was a lecturer of computer science. Since September 1986 he has been a professor of computer science at East China Normal University. He is currently working at Oxford University Computing Laboratory. His major research interests are programming language semantics, software engineering and distributed computing. Tony Hoare is Professor of Computation at the University of Oxford. He received his MA from Oxford in Classical Languages, Literature, History and Philosophy. He worked for eight years as programmer, manager and research scientist with a small computer manufacturer. He is the recipient of several honours for his contributions to the study of computer programming languages, and is generally famed for Hoare's Law: inside every large program there is a small program trying to get out.     

增强灰狼自适应阈值去噪法在变压器套管引线超声检测中的应用

超声检测作为一种新型的电力设备检测方式,回波信号极易受到噪声干扰,为了提高信号质量,提出了一种增强灰狼自适应阈值去噪法。首先,对信号进行多尺度小波分解,引入基于梯度下降自适应阈值法,用于估计不同小波分解层的阈值大小。然后改进传统灰狼算法的变异策略与收敛因子,优化自适应阈值函数梯度值,确定最优阈值并完成信号去噪。对仿真及实测超声回波信号的去噪结果表明,经本算法去噪后,超声回波信号的起振位置等有效信息得以保留,信号信噪比更高,均方误差更小,运行时间更短。本算法应用到变压器套管引线超声检测中,可提高超声检测的准确性,获取了变压器套管引线的状态,具有一定的应用价值。     

Testing Congruence for Mobile Processes

Testing equivalence on πprocesses has been studied in literature.The equivalence is not closed under the iuput prefix operator and is therefore not a congruence relation.This note takes a look at testing congruence on fipite π processes.A complete equational system is given for the congruence relation.     

中医肺病科电子病历系统设计与实现

中医肺病科电子病历系统的设计是为了将中医院肺病科的纸质病历转化为电子病历,从而更好的保存、使用中医肺病科病历,并为以后进行中医肺病数据的分析研究做前期准备。系统用Java Web技术进行开发,采用MVC设计模式,并用struts框架简化开发步骤。最终开发出的中医肺病科电子病历系统拥有快捷简单的操作界面,图文并茂的病历显示功能和方便的病历打印功能。