54,820 research outputs found

    Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases

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    This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, as search strategies, and the generalized F-score (GF) to evaluate the importance of each feature. The new accuracy measure is used as the criterion to evaluated the performance of a temporary SVM to direct the feature selection algorithms. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the stable and efficient classifiers. To get the stable, statistical and optimal classifiers, we conduct 10-fold cross validation experiments in the first stage; then we merge the 10 selected feature subsets of the 10-cross validation experiments, respectively, as the new full feature set to do feature selection in the second stage for each algorithm. We repeat the each hybrid feature selection algorithm in the second stage on the one fold that has got the best result in the first stage. Experimental results show that our proposed two-stage hybrid feature selection algorithms can construct efficient diagnostic models which have got better accuracy than that built by the corresponding hybrid feature selection algorithms without the second stage feature selection procedures. Furthermore our methods have got better classification accuracy when compared with the available algorithms for diagnosing erythemato-squamous diseases

    Recent Trends in Hospitalization for Acute Myocardial Infarction in Beijing: Increasing Overall Burden and a Transition From ST-Segment Elevation to Non-ST-Segment Elevation Myocardial Infarction in a Population-Based Study

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    Comparable data on trends of hospitalization rates for ST-segment elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI) remain unavailable in representative Asian populations.To examine the temporal trends of hospitalization for acute myocardial infarction (AMI) and its subtypes in Beijing.Patients hospitalized for AMI in Beijing from January 1, 2007 to December 31, 2012 were identified from the validated Hospital Discharge Information System. Trends in hospitalization rates, in-hospital mortality, length of stay (LOS), and hospitalization costs were analyzed by regression models for total AMI and for STEMI and NSTEMI separately. In total, 77,943 patients were admitted for AMI in Beijing during the 6 years, among whom 67.5% were males and 62.4% had STEMI. During the period, the rate of AMI hospitalization per 100,000 population increased by 31.2% (from 55.8 to 73.3 per 100,000 population) after age standardization, with a slight decrease in STEMI but a 3-fold increase in NSTEMI. The ratio of STEMI to NSTEMI decreased dramatically from 6.5:1.0 to 1.3:1.0. The age-standardized in-hospital mortality decreased from 11.2% to 8.6%, with a significant decreasing trend evident for STEMI in males and females (P < 0.001) and for NSTEMI in males (P = 0.02). The rate of percutaneous coronary intervention increased from 28.7% to 55.6% among STEMI patients. The total cost for AMI hospitalization increased by 56.8% after adjusting for inflation, although the LOS decreased by 1 day.The hospitalization burden for AMI has been increasing in Beijing with a transition from STEMI to NSTEMI. Diverse temporal trends in AMI subtypes from the unselected "real-world" data in Beijing may help to guide the management of AMI in China and other developing countries

    High-sensitivity microfluidic calorimeters for biological and chemical applications

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    High-sensitivity microfluidic calorimeters raise the prospect of achieving high-throughput biochemical measurements with minimal sample consumption. However, it has been challenging to realize microchip-based calorimeters possessing both high sensitivity and precise sample-manipulation capabilities. Here, we report chip-based microfluidic calorimeters capable of characterizing the heat of reaction of 3.5-nL samples with 4.2-nW resolution. Our approach, based on a combination of hard- and soft-polymer microfluidics, provides both exceptional thermal response and the physical strength necessary to construct high-sensitivity calorimeters that can be scaled to automated, highly multiplexed array architectures. Polydimethylsiloxane microfluidic valves and pumps are interfaced to parylene channels and reaction chambers to automate the injection of analyte at 1 nL and below. We attained excellent thermal resolution via on-chip vacuum encapsulation, which provides unprecedented thermal isolation of the minute microfluidic reaction chambers. We demonstrate performance of these calorimeters by resolving measurements of the heat of reaction of urea hydrolysis and the enthalpy of mixing of water with methanol. The device structure can be adapted easily to enable a wide variety of other standard calorimeter operations; one example, a flow calorimeter, is described

    Extending twin support vector machine classifier for multi-category classification problems

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    © 2013 – IOS Press and the authors. All rights reservedTwin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification problems, but also it is four times faster in training a classifier than classical support vector machines. This paper proposes one-versus-all twin support vector machine classifiers (OVA-TWSVM) for multi-category classification problems by utilizing the strengths of TWSVM. OVA-TWSVM extends TWSVM to solve k-category classification problems by developing k TWSVM where in the ith TWSVM, we only solve the Quadratic Programming Problems (QPPs) for the ith class, and get the ith nonparallel hyperplane corresponding to the ith class data. OVA-TWSVM uses the well known one-versus-all (OVA) approach to construct a corresponding twin support vector machine classifier. We analyze the efficiency of the OVA-TWSVM theoretically, and perform experiments to test its efficiency on both synthetic data sets and several benchmark data sets from the UCI machine learning repository. Both the theoretical analysis and experimental results demonstrate that OVA-TWSVM can outperform the traditional OVA-SVMs classifier. Further experimental comparisons with other multiclass classifiers demonstrated that comparable performance could be achieved.This work is supported in part by the grant of the Fundamental Research Funds for the Central Universities of GK201102007 in PR China, and is also supported by Natural Science Basis Research Plan in Shaanxi Province of China (Program No.2010JM3004), and is at the same time supported by Chinese Academy of Sciences under the Innovative Group Overseas Partnership Grant as well as Natural Science Foundation of China Major International Joint Research Project (NO.71110107026)

    Study of J/ψJ/\psi Production in sNN=200\sqrt{s_{NN}} = 200 GeV p+pp+p and d+Aud+Au Collisions in PHENIX

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    J/ψJ/\psi measurements in p+pp+p and d+Aud+Au collisions serve as crucial references to understand the J/ψJ/\psi production in Au+AuAu+Au collisions at RHIC where quark gluon plasma (QGP) is expected to be formed. They also provide important clues to study various interesting phenomena such as the gluon shadowing and color glass condensate. We report the latest results from PHENIX experiment on J/ψJ/\psi production in p+pp+p and d+Aud+Au collisions at forward, backward and midrapidity.Comment: 4pages, 6 figures, proceeding of the 18th International Conference on Nucleus-Nucleus Collisions (QM2005), Aug.4-9, Budapest, Hungar
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