“十一五”期间我国中医外科学研究热点分析

以中国学术文献网络出版总库2006-2010年间的329篇中医外科文献为分析对象,基于文献聚类及高频关键词的社会网络分析方法,对文献主题及相关主题词进行系统分析,揭示"十一五"期间中医外科在教学、临床等方面的研究热点与主题分布。     

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.     

集束化护理提高小耳畸形患者再造耳清洁度的效果观察

目的 探索集束化护理提高小耳畸形清洁度的临床效果.方法 2019年1月成立品管圈小组,通过对再造耳清洁度的现况调查,分析影响清洁度的因素,设定目标、拟定对策并实施.结果 开展品管圈活动后,再造耳的清洁度得分由53分上升到81分,目标达成率为105％,进步率为53％.小组成员从解决问题的能力、积极性、品管圈手法掌握程度、...     

基于SPSS的共现聚类分析参数选择的实例研究

以OHSUMED语料库内提供的明确相关提问对为金标准和研究材料,借助BICOMB软件生成主题词-来源文献矩阵和共词矩阵,并获得各种系数的相似（相异）矩阵,对比分析目前国内基于SPSS共现聚类分析过程中主题词-来源文献矩阵与共现矩阵、各种相似性参数和各种类间距离计算方法的聚类效果。结果表明：主题词-来源文献矩阵聚类结果优于共词矩阵,在聚类分析中应优先选择。共词矩阵选择相似系数时应结合实际矩阵数据性质,并注意聚类方法原理上的正确性。     

基于共词分析的医学伦理学领域主题热点及演进态势

提出了一种主题知识关联指标测度与主题知识网络图谱相结合的领域主题关联特征挖掘方法。融合聚类分析、多维尺度分析和战略坐标分析,形成基于共词分析的分阶段、多层面领域主题热点及演进态势研究方法体系。以医学伦理学为例,进行实证研究,从而识别领域主题、揭示各子主题内部以及主题之间的知识连接特性、探测主题发展态势,为科研人员和决策人员提供科学的决策理论支撑和方法。     

滑动时间窗视角下科学基金研究前沿探测

研究前沿是某领域处于先锋地位的研究内容。想要更好的理解当下和未来的研究趋势,需要时刻关注领域的研究前沿。文章综述了前人的研究方法,指出其共有问题,提出滑动时间窗视角下基于科学基金的前沿探测方法。并在选取最优聚类算法的基础上,以肿瘤学为例探测其研究前沿。结果表明Agglomerative的聚类效果最优,对4个滑动时间窗内的数据进行共词聚类分析,探测其研究前沿并分析其演化规律。     

云南省玉龙县纳西族心血管疾病危险因素聚集性的社会经济影响研究

目的分析云南省玉龙县纳西族居民心血管疾病危险因素聚集性的社会经济影响因素.方法采用分层随机抽样的方法抽取1 402名≥35岁的纳西族村民进行问卷调查和现场体检.结果该地村民超重、肥胖、中心性肥胖、吸烟、饮酒、心血管疾病家族史、高盐饮食、被动吸烟、生活压力、情绪低落和缺乏体力活动的检出率、高血压和糖尿病的患病率分别为26.0%、8.9%、39.5%、32.9%、24.5%、10.34%、27.4、21.8%、9.3%、5.2%、32.2%,26.2%和4.1%;仅有6.2%的纳西族居民无心血管疾病危险因素聚集性;男性具有3个及以上危险因素的比例大于女性(P<0.01);户主、文化程度越高者和综合社会经济地位越高者的纳西族居民的心血管疾病危险因素聚集度越低(P<0.05).结论云南省纳西族居民存在较高的心血管疾病危险因素聚集性.应重点加强低社会经济地位以及低受教育水平纳西族居民的心血管疾病的预防和干预工作.     

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.     

陕西省黄陵县克山病家庭的流行病学调查分析

目的 探讨陕西省黄陵县克山病家庭的克山病患病聚集性。方法 随机抽取陕西省黄陵县店头镇及下属各乡的52户克山病家庭，对患者及亲属进行一般情况、临床表现、病程等疾病相关流行病学调查及分析。结果 黄陵县克山病亲属的克山病检出率（12．30％）明显高于2002年陕西省克山病的检出率（3．66％，χ^2=37.55，P〈0．01），也明显高于以往当地一般人群的检出率（6．37％，χ^2=13.11，P〈0.01）。家庭检出率在6个村都明显高于2002年的各村人群检出率。结论 调查发现克山病存在家庭聚集性。     

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.