利用NOVELL网络实现高频心电图的聚类分析

高频心电图模式识别所需的聚类分析因及复杂而较难实现。本文介绍一种利用ＮＯＶＥＬＬ网络实现聚类分析的方法。在管理员工作站的具体组织与控制下，网上的其它工作站利用空闲机时将聚类分析化整为零，分布处理，而各工作站的日常工作几乎不会受到影响。     

内镜图文工作站网络信息系统的设计与实现

目的：建立一种内镜图文工作站网络信息网络系统,集中管理医院内镜图文信息,帮助医生实现远程查询、远程诊断等功能。方法：利用局域网将医院所有内镜图文工作站与服务器链接,服务器负责收集所有工作站的信息,医生利用服务器完成远程查询、远程诊断等功能。结果：医院实现了内镜图文信息的统一管理,医生利用系统实现了远程病例查询和远程诊断。结论：该系统可视为医院信息系统重要组成部分。     

平煤总医院门诊医生站的建设

门诊医生工作站是门急诊医生借助计算机网络，实现患者信息采集、处理、存储、传输和服务，实现对门急诊患者信息管理的计算机软件系统。可以实现医疗管理信息和数字化，从而提高医院的管理水平、医生的工作效率和服务质量。本文介绍了实施门诊医生工作站流程及重点讨论了利用门诊医生工作站对提高门诊服务质量的几点体会。     

多功能DSA影像工作站的设计和开发

介绍了一种多功能DSA影像工作站的主要结构,重点论述了软件系统的构成。影像工作站有图像采集、数据传输、存档、处理等功能。利用客户端/服务器软件模块,实现医学图像的数字化、传输和处理。     

PACS影像诊断工作站的管理与维护

目的：探讨PACS影像诊断工作站的管理与维护。方法：采用××公司的PACS系统，影像科接入PACS系统的设备有CT、MRI、CR、DR及各种造影机共16台，所得影像数据均符合DICOM3．0标准，影像科安装影像诊断工作站36台。在远程控制工作站上利用Radmim远程管理工具，实现对所有影像诊断工作站的管理与维护。结果：经12个月的运行，实现了放射科影像诊断工作站的远程管理和控制。结论：影像诊断工作站是PACS系统的末端设备，是影像科室医师工作的主要工具，其管理与维护是确保临床诊断工作顺利进行、PACS系统安全稳定的重要环节之一。     

开发MV300工作站的DICOM打印功能

本文详细介绍了如何打开MV300工作站的DICOM打印功能,实现利用MV300直接连接激光相机和洗片机,打印胶片。     

依托数字化医院平台优化体检工作流程

随着社会的进步,体检越来越受到人们的重视。医院体检工作涉及部门多,存在信息沟通不及时、易与病人就医工作冲突的矛盾;体检项目随需求而变,随体检过程而变,造成体检项目结果手工汇总费时、费力、易出差错。籍助数字化医院平台,合理规范体检流程是提高体检工作效率和服务质量的必然选择。分析人们对健康体检需求,给出由登记工作站、体检单元工作站、主检医生工作站、健康档案分发工作站、体检系统维护工作站组成的体检信息系统的功能模型和岗位模型。提出从登记工作站发出体检项目指令,依托全院范围的LIS、PACS系统实现体检检查、检验与就医检查、检验在流程、数据和软件平台的一致;在门诊医生工作站、住院医生工作站硬件上安装体检单元工作站,供分科医生使用,实现体检单元虚拟化;由主检医生工作站导入检查诊断报告和检验结果报告,汇总各体检单元项目结果,对各科病史、阳性体征、异常结果、分科小结、分科建议的浏览、审核和摘录,形成体检建议指导和体检总结;由健康档案分发工作站打印分发体检者健康档案。同时,文中给出了优化后的健康体检工作流程。     

基于“军卫一号”的多工作站登录转换系统的设计与实现

目的：快速改变用户归属科室,从而实现对多个工作站的登录。方法：利用现有网络资源,以PowerBuider9.0为基本的开发工具编写程序代码。结果：设计并开发了适用于＂军卫一号＂数据库的多工作站登录转换系统。结论：该系统界面友好、操作简单、实用性强。     

急诊综合信息系统的设计与开发

目的研究与开发急诊综合信息系统．以实现急诊病人信息的采集、加工、存储、传输和利用的自动化。方法通过建立急诊医疗信息流程模型、急诊病人主要信息数据库,开发急诊工作站、留观病房医护工作站、输液管理工作站等系统,实现急诊救治工作的数字化。结论系统的开发与应用,明显缩短了病人救治时间,推进了急诊医疗管理的信息化进程。结果（1）提出了综合性医院急诊医疗信息管理所要求的基本数据项;（2）提出了急诊科急救医疗信息流程模式与管理方法;（3）开发了急诊综合信息系统软件。     

静脉药物配置中心信息管理系统的构建与应用

目的建立适合静脉药物配置中心工作和管理的信息系统。方法利用医院原有HIS系统中可利用的部分功能模块,改造临床工作站,设计适合配置中心工作流程的功能模块。结果建立静脉药物配置中心信息管理系统,其模块功能适用,与医院原有HIS系统完全兼容,运行稳定,实现了静脉药物配置中心与临床工作站的信息化传递。结论该系统的功能适用,运行可靠,确保了我院静脉药物配置中心成功运行。     

数据挖掘技术在生物信息学基因变异规律研究中的应用

目的聚类分析中适宜类别数的确定和结果的验证一直是难以解决的问题，而在生物信息学研究中这一矛盾尤为突出，本文试图将数据挖掘技术引入这一领域，以期能有所帮助。方法以甲型流感病毒的H3序列为例，按照数据挖掘的思想将其拆分为训练集和验证集，然后使用两阶段聚类法和自组织图进行聚类分析，利用验证集对聚类结果进行验证，并进行各类的特征描述。结果两阶段聚类法可自动搜索适宜的类别数，两种聚类方法结果相互验证，以及验证集的结果验证都证实了聚类结果的准确性。结论数据挖掘方法体系中的智能聚类技术可以满足基因序列数据聚类问题的需求，其相关技术可较好的解决类别数判定、结果验证等问题，值得在该领域中推广。     

Optimal selection of the spatial scan parameters for cluster detection: a simulation study

Circular and elliptic spatial scan statistics requires the user to choose a maximum cluster size. A common value for this parameter is 50% of the underlying population. In addition to the detected primary cluster, the user may be interested in the analysis of significant secondary clusters. It can also be argued that if the true cluster is irregular, then choosing a small value for the maximum cluster size and evaluating significant secondary clusters may improve cluster detection and avoid the use of irregular cluster methods. This work explores the performance of the circular, elliptic and double scan statistics for different values of the maximum cluster size and different options for the analysis of secondary clusters. Empirical results show that for hot-spot clusters, the analysis of secondary clusters which are statistically significant do not improve the detection of the true unknown cluster, on average. There is evidence that a variable maximum cluster size improves performance. That is, the double scan statistic applies an early-stopping procedure which improves positive predictive values.     

基于聚类分析的集中式供水水质安全风险评价

目的探讨适宜于集中式供水领域特点的风险评价方法。方法选择我国1 302座集中式供水设施,并计算其风险指数。采用两阶段聚类计算最优分类数。采用系统聚类和K-means聚类两种方法,并以最优分类数和标准推荐的三分类分别对研究对象的风险指数进行聚类分析。结果两阶段聚类显示最优分类数为4类;四分类系统聚类结果显示,97.8%的对象为2类;三分类系统聚类结果显示,98.5%的对象为1类;四分类的K-means聚类结果显示,14.0%、16.0%、30.6%和39.5%的对象被分别分为1~4类;三分类聚类结果显示,26.3%、34.2%和39.5%的对象被分别分为1~3类。结论集中式供水风险评价适宜分为分高、较高、较低、低四类;K-means聚类方法更适宜应用于该领域。     

Accounting for cluster randomization: a review of primary prevention trials, 1990 through 1993.

OBJECTIVES. This methodological review aims to determine the extent to which design and analysis aspects of cluster randomization have been appropriately dealt with in reports of primary prevention trials. METHODS. All reports of primary prevention trials using cluster randomization that were published from 1990 to 1993 in the American Journal of Public Health and Preventive Medicine were identified. Each article was examined to determine whether cluster randomization was taken into account in the design and statistical analysis. RESULTS. Of the 21 articles, only 4 (19%) included sample size calculations or discussions of power that allowed for clustering, while 12 (57%) took clustering into account in the statistical analysis. CONCLUSIONS. Design and analysis issues associated with cluster randomization are not recognized widely enough. Reports of cluster randomized trials should include sample size calculations and statistical analyses that take clustering into account, estimates of design effects to help others planning trials, and a table showing the baseline distribution of important characteristics by intervention group, including the number of clusters and average cluster size for each group.     

医院综合效益评价分档的比较研究

目的为医院综合效益乃至医学研究中其他指标分档提供较合理的方法。方法综合效益分析采用层次分析法(AHP),评价分档采用均值标准差法与聚类分析法进行比较研究。结果聚类分析法分档结果相对较分散、合理;均值标准差法分档结果不甚理想,且大部分集中在低等。结论聚类分析法在医院管理及各类医学指标分类中值得推广。     

关于数理统计中系统聚类法的讨论

目的 研究聚类分析中系统聚类法的某些聚类过程对聚类结果产生的干扰，寻找消除该干扰的聚类过程。方法 利用图论和模糊数学中的最大树聚类法为标准，对不同的聚类过程进行分析，找出系统聚类法中某些聚类过程给聚类结果带来的严重影响的原因。结果给出能消除系统聚类法中某些聚类过程给聚类结果带来严重影响的统一的（指标或样品）聚类过程。结论 统一的（指标或样品）聚类过程消除了系统聚类法中某些聚类过程给聚类结果带来的严重影响；不但保留了系统聚类法中聚类过程的优点，而且还能挖掘出隐藏在原始数据中的有用信息。     

聚类与判别分析在大学生SCL-90测试结果中的运用

目的 分析探索SCL-90结果解释中判别效果较好的聚类指标.方法 对某高校3638名新生SCL-90测试结果进行K-均值聚类及逐步判别分析.结果 采用K-均值聚类的方法分类,将测试结果分成3组,各组之间差异具有统计学意义.其次,通过逐步判别分析,在11个聚类指标中,选出8个有统计学意义的指标构建判别模型,用此模型对3638个新生样本作回顾性评价,误判率为2.9%.结论 逐步判别分析发现,通过SCL-90中的强迫症状、人际敏感、抑郁、敌对、恐怖、偏执、精神病性7个因子分及总均分进行聚类分析的判别效果较好.     

Dietary patterns in the Southampton Women's Survey

OBJECTIVE: Dietary pattern analysis is receiving increasing attention as a means of summarizing the multidimensional nature of dietary data. This research aims to compare principal component analysis (PCA) and cluster analysis using dietary data collected from young women in the UK. DESIGN: Diet was assessed using a 100-item interviewer-administered food frequency questionnaire. PCA and cluster analysis were used to examine dietary patterns. SETTING: Southampton, UK. SUBJECTS: A total of 6125 non-pregnant women aged 20-34 years. RESULTS: PCA identified two important patterns: a 'prudent' diet and a 'high-energy' diet. Cluster analysis defined two clusters, a 'more healthy' and a 'less healthy' cluster. There was a strong association between the prudent diet score and the two clusters, such that the mean prudent diet score in the less healthy cluster was -0.73 standard deviations and in the more healthy cluster was +0.83 standard deviations; the difference in the high-energy diet score between the two clusters was considerably smaller. CONCLUSIONS: Both approaches revealed a similar dietary pattern. The continuous nature of the outcome of PCA was considered to be advantageous compared with the dichotomy identified using cluster analysis. SPONSORSHIP: The study was funded by the Dunhill Medical Trust, the University of Southampton and the Medical Research Council.     

Inhalable airborne particulate pollution in sites characterized by dissimilar environmental conditions

The aim of the present study is to characterise from a physico-chemical point of view the airborne particulate matter (PM10) sampled in different sites dissimilar with regard to the weather conditions, the residential density and the industrial activities. The particles were collected by an eight-stage cascade impactor, Andersen particle fractionating sampler. The analysis of the particle samples was performed by a scanning electron microscopy (SEM) equipped with a thin-window system for X-ray microanalysis by energy dispersion spectrometry. The hierarchical cluster analysis (HCA) on the analytical data revealed the presence of eight different particulate types (particle clusters) in the sampling sites: C-rich particles (cluster 1); Ca and Mg carbonates (cluster 2); Ca sulphates (cluster 3); silica particles (cluster 4); silicates (cluster 5); Fe-rich particles (cluster 6); metal compounds (cluster 7); carbonaceous particles with heavy metals (cluster 8). Data obtained in this study demonstrate a significant correlation between the PM10 composition and the anthropic activities present the sampling site.     

Empirically derived eating patterns using factor or cluster analysis: a review

This paper reviews studies performed to date that have employed cluster or factor analysis to empirically derive eating patterns. Since 1980, at least 93 studies were published that used cluster or factor analysis to define dietary exposures, of which 65 were used to test hypotheses or examine associations between patterns and disease outcomes or biomarkers. Studies were conducted in diverse populations across many countries and continents and suggest that patterns are associated with many different biomarkers and disease outcomes, whether measured by cluster or factor analysis. Despite clear differences in approaches and interpretations, there is some evidence that underlying eating patterns are revealed by either method. Although the research considered herein has created a meaningful body of literature, refining both the factor and cluster analysis methods will help to further establish eating patterns as a sound dietary assessment method.