A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

How Meta-Analysis Increases Statistical Power.

One of the most frequently cited reasons for conducting a meta-analysis is the increase in statistical power that it affords a reviewer. This article demonstrates that fixed-effects meta-analysis increases statistical power by reducing the standard error of the weighted average effect size (T?.) and, in so doing, shrinks the confidence interval around T?.. Small confidence intervals make it more likely for reviewers to detect nonzero population effects, thereby increasing statistical power. Smaller confidence intervals also represent increased precision of the estimated population effect size. Computational examples are provided for 3 effect-size indices: d (standardized mean difference), Pearson's r, and odds ratios. Random-effects meta-analyses also may show increased statistical power and a smaller standard error of the weighted average effect size. However, the authors demonstrate that increasing the number of studies in a random-effects meta-analysis does not always increase statistical power. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

韶钢第五轧钢厂自动分槽器的设计与应用

介绍了韶钢第五轧钢厂自动分槽器的设计与应用．     

Uncertainty Matters: Computer Models at the Science–Policy Interface

The use of computer models offers a general and flexible framework that can help to deal with some of the complexities and difficulties associated with the development of water management plans as prescribed by the Water Framework Directive. However, despite the advantages modelling presents, the integration of information derived from models into policy is far away from being trivial or the norm. Part of the difficulties of this integration is rooted in the lack of confidence policy makers have on the incorporation of modelling information into policy formulation. In this paper we examine the reasons for this apparent lack of confidence and explore how some tools, presently in use, address this problem. We conclude that public confidence in models is highly dependent on the way uncertainties are addressed and suggest possible directions of action to improve the current situation. Four real case studies illustrate how computer models have been used in The Netherlands for carrying out management plans at regional and national scale. We suggest that the solution to integrate modelling information into policy formulation lies on both the modelling and the policy-making communities.     

F150交换机告警关联规则的研究

为了提高电信网络运营效率，降低维护成本，电信运营企业在网络告警管理系统中需要引入数据挖掘技术。本文主要研究利用数据挖掘技术来进行故障的管理，即对告警进行关联性分析，详细分析了告警序列数据的关联规则挖掘算法，并在南昌本地网F150交换机的告警数据库中予以实现，同时对实际挖掘结果进行分析和整理，推导出一些实用的关联规则。     

Class normalization in centroid-based text categorization

Centroid-based categorization is one of the most popular algorithms in text classification. In this approach, normalization is an important factor to improve performance of a centroid-based classifier when documents in text collection have quite different sizes and/or the numbers of documents in classes are unbalanced. In the past, most researchers applied document normalization, e.g., document-length normalization, while some consider a simple kind of class normalization, so-called class-length normalization, to solve the unbalancedness problem. However, there is no intensive work that clarifies how these normalizations affect classification performance and whether there are any other useful normalizations. The purpose of this paper is three folds; (1) to investigate the effectiveness of document- and class-length normalizations on several data sets, (2) to evaluate a number of commonly used normalization functions and (3) to introduce a new type of class normalization, called term-length normalization, which exploits term distribution among documents in the class. The experimental results show that a classifier with weight-merge-normalize approach (class-length normalization) performs better than one with weight-normalize-merge approach (document-length normalization) for the data sets with unbalanced numbers of documents in classes, and is quite competitive for those with balanced numbers of documents. For normalization functions, the normalization based on term weighting performs better than the others on average. For term-length normalization, it is useful for improving classification accuracy. The combination of term- and class-length normalizations outperforms pure class-length normalization and pure term-length normalization as well as unnormalization with the gaps of 4.29%, 11.50%, 30.09%, respectively.     

Distinctive Features of Minimization of a Risk Functional in Mass Data Sets

A statistical learning model is considered within the framework of the theory of uniform convergence of frequencies of errors in the case where the convergence is violated as a result of increasing the informativeness of training examples. Drawbacks of nonconstructive refinements of Vapnik-Chervonenkis estimates based on an assumption on the distribution law of violations are shown. A new approach to obtaining constructive estimates for mass data sets is proposed.     

隧洞开挖网络进度的优化研究

本文以单项工程为研究对象,以隧洞工程为例,研究了单项工程施工所需用的时间。文中以施工单位所具有的资源,结合施工工作环境、作业空间为约束条件,以费用最小为目标函数,建立了优化模型。由资源的工作性能指标,利用模糊数学原理,对资源间的匹配程度作了定量分析,并进一步推出了不同匹配程度的资源对作业时间的影响的计算式。并从影响各工序完工时间的因素的随机性出发,利用数理统计理论,由各工序的细分活动的规律推出了各工序的完工需用时间的概率表达式,解决了工期与置信度间的关系。最后结合工程实例,求出了一定置信度下,不同资源供应时的最优工期。     

Predicting postdischarge opiate abstinence from admission measures of motivation and confidence.

Admission scores from a model comprising 3 motivation factors concerning the cessation of heroin use and a confidence scale concerning postdischarge abstinence were tested for their ability to predict postdischarge outcomes in patients beginning inpatient opiate detoxification. Statistically significant prediction of abstinence from heroin 30 days after discharge and the number of heroin-free days in the 3 months following admission was based on the confidence scale and a factor concerned with externally imposed constraints on continued heroin use. The single-scale confidence measure made the largest contribution to each prediction, indicating that such scales may be potentially useful outcome predictors for postdischarge abstinence. External constraints on heroin use may not provide a basis for success in this treatment modality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于可变大小模板的改进图像修复算法

Criminisi等人提出的基于模板的图像修复算法是该领域最重要的贡献之一。在保持Criminisi基本算法框架不变的基础上,本文提出了一些改进思想。主要贡献是设计了基于可变大小模板的块匹配程序以使模板搜索更准确灵活。此外,对信度更新方式进行了修正,并结合局部搜索以提高算法的综合性能。实验结果证实了改进的有效性。