医用粘胶相关性皮肤损伤的文献计量学分析

目的: 应用文献计量学方法,分析国内外医用粘胶相关性皮肤损伤（Medical adhesive-related skin injuries,MARSI）文献的研究主题和热点。方法: 检索Cochrane Library、PubMed、EMbase、Web of science、sinoMed、CNKI、Wanfang、VIP等数据库MARSI相关文献,检索时限为建库至2019年5月。采用SPSS 22.0 软件进行文献计量分析,利用gCLUTO统计对国内外高频主题词进行聚类分析。结果: 共纳入论文103篇,其中外文文献占总文献量的27.18%,中文文献占总文献量的72.82%。聚类分析将国内外高频主题词聚为4大类,依次为皮肤损伤及损伤类型、相关危险因素、风险评估及风险管理、护理干预措施。结论: MARSI仍是国内外研究的热点,其发病机制研究仍处于探索阶段,目前尚缺乏有效的预防策略,研究领域较局限。在今后的研究中,应加强MARSI预防的研究、注重多学科合作。     

Grid topologies for the self-organizing map

The original Self-Organizing Feature Map (SOFM) has been extended in many ways to suit different goals and application domains. However, the topologies of the map lattice that we can found in literature are nearly always square or, more rarely, hexagonal. In this paper we study alternative grid topologies, which are derived from the geometrical theory of tessellations. Experimental results are presented for unsupervised clustering, color image segmentation and classification tasks, which show that the differences among the topologies are statistically significant in most cases, and that the optimal topology depends on the problem at hand. A theoretical interpretation of these results is also developed.     

Grouping of body areas affected in traffic accidents. A cohort study

Background

Traffic accidents are considered a public health problem and, according to the World Health Organization, currently is the eighth cause of death in the world. Specifically, pedestrians, cyclists and motorcyclists contribute half of the fatalities. Adequate clinical management in accordance with aggregation patterns of the body areas involved, as well as the characteristics of the accident, will help to reduce mortality and disability in this population.

基于关联规则与熵聚类的清热类中成药组方规律研究

目的:探讨常用清热类中成药组方规律。方法:收录《新编国家中成药》中清热类中成药处方,采用关联规则Apriori算法和复杂系统熵聚类等方法,确定处方中药物的使用频次及药物之间的关联规则等。结果:高频次药物包括甘草、黄芩、冰片、金银花、大黄、连翘等;高频次药物组合包括"甘草、黄芩""甘草、桔梗""连翘、金银花"等;置信度较高的关联规则包括"牛黄->冰片""桔梗->甘草""朱砂->冰片""栀子->黄芩"等。结论:处方用药中除常见的清热类中药外,尚包括具有清热作用的部分开窍药、安神药、泻下药及其他类药物。     

我国卫生区域划分研究

目的:我国不同地区卫生资源发展不平衡,制定区域卫生规划要综合考虑不同地区的经济、社会、人口、健康等方面的状况。方法:利用统计年鉴数据,考虑经济、社会、人口、健康等因素,分别通过主成分分析和因子分析方法计算不同地区的综合得分,然后对综合得分进行聚类分析。结果:主成分分析综合得分和因子分析综合得分的聚类分析将全国以省(自治区、直辖市)为单元划分为6类地区,两种分析方法的分类结果类似。结论:将全国卫生区域分为六类,第一类地区:上海市、北京市;第二类地区:天津市;第三类地区:江苏省、浙江省、广东省;第四类地区:辽宁省、山东省、福建省、内蒙古自治区、吉林省、重庆市、陕西省、湖北省、黑龙江省、山西省、河南省、湖南省、河北省、海南省、四川省、江西省、安徽省;第五类地区为:广西壮族自治区、宁夏回族自治区、新疆维吾尔自治区、甘肃省、青海省、贵州省、云南省;第六类地区为:西藏自治区。分类结果基本符合中国31个省(自治区、直辖市)的卫生发展实际。     

Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances

The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This paper presents a self-organizing map for interval-valued data based on adaptive Mahalanobis distances in order to do clustering of interval data with topology preservation. Two methods based on the batch training algorithm for the self-organizing maps are proposed. The first method uses a common Mahalanobis distance for all clusters. In the second method, the algorithm starts with a common Mahalanobis distance per cluster and then switches to use a different distance per cluster. This process allows a more adapted clustering for the given data set. The performances of the proposed methods are compared and discussed using artificial and real interval data sets.     

Deriving reference values for nerve conduction studies from existing data using mixture model clustering

Objective: to obtain locally valid reference values (RVs) from existing nerve conduction study (NCS) data.Methods: we used age, sex, height and limb temperature-based mixture model clustering (MMC) to identify normal and abnormal measurements on NCS data from two university hospitals. We compared MMC-derived RVs to published data; examined the effect of using different variables; validated MMC-derived RVs using independent data from 26 healthy control subjects and investigated their clinical applicability for the diagnosis of polyneuropathy.Results: MMC-derived RVs were similar to published RVs. Clustering can be achieved using only sex and age as variables. MMC is likely to yield reliable results with fewer abnormal than normal measurements and when the total number of measurements is at least 300. Measurements from healthy controls fell within the 95% MMC-derived prediction interval in 97.4% of cases.Conclusions: MMC can be used to obtain RVs from existing data, providing a locally valid, accurate reflection of the (ab)normality of an NCS result.Significance: MMC can be used to generate locally valid RVs for any test for which sufficient data are available.¹     

高等军事医学院校教师工作绩效评价

[目的]通过对2所高等军事医学院校教师相关指标进行调查,探讨建立高等军事医学院校人才评价方法及评价指标体系.[方法]运用两因素等级相关分析、指标聚类分析,确定有代表性的指标；用矩阵法求个人综合得分划分层次.[结果]入选的8项评价指标为:军区或总部级研究所或中心、全军级研究所或中心、为首国内核心期刊发表论文数等.[结论]在高等军事医学院校进行教师人才评价及选拔优秀人才过程中,该评价方法及指标体系是可行的.     

A divide-and-combine method for large scale nonparallel support vector machines

Nonparallel Support Vector Machine (NPSVM) which is more flexible and has better generalization than typical SVM is widely used for classification. Although some methods and toolboxes like SMO and libsvm for NPSVM are used, NPSVM is hard to scale up when facing millions of samples. In this paper, we propose a divide-and-combine method for large scale nonparallel support vector machine (DCNPSVM). In the division step, DCNPSVM divide samples into smaller sub-samples aiming at solving smaller subproblems independently. We theoretically and experimentally prove that the objective function value, solutions, and support vectors solved by DCNPSVM are close to the objective function value, solutions, and support vectors of the whole NPSVM problem. In the combination step, the sub-solutions combined as initial iteration points are used to solve the whole problem by global coordinate descent which converges quickly. In order to balance the accuracy and efficiency, we adopt a multi-level structure which outperforms state-of-the-art methods. Moreover, our DCNPSVM can tackle unbalance problems efficiently by tuning the parameters. Experimental results on lots of large data sets show the effectiveness of our method in memory usage, classification accuracy and time consuming.