An Automatic Interaction Detection Hybrid Model for Bankcard Response Classification

Data mining techniques have numerous applications in bankcard response modeling. Logistic regression has been used as the standard modeling tool in the financial industry because of its almost always desirable performance and its interpretability. In this paper, we propose a hybrid bankcard response model, which integrates decision tree-based chi-square automatic interaction detection (CHAID) into logistic regression. In the first stage of the hybrid model, CHAID analysis is used to detect the possible potential variable interactions. Then in the second stage, these potential interactions are served as the additional input variables in logistic regression. The motivation of the proposed hybrid model is that adding variable interactions may improve the performance of logistic regression. Theoretically, all possible interactions could be added in logistic regression and significant interactions could be identified by feature selection procedures. However, even the stepwise selection is very time-consuming when the number of independent variables is large and tends to cause the p \u3e\u3e n problem. On the other hand, using CHAID analysis for the detection of variable interactions has the potential to overcome the above-mentioned drawbacks. To demonstrate the effectiveness of the proposed hybrid model, it is evaluated on a real credit customer response data set. As the results reveal, by identifying potential interactions among independent variables, the proposed hybrid approach outperforms the logistic regression without searching for interactions in terms of classification accuracy, the area under the receiver operating characteristic curve (ROC), and Kolmogorov-Smirnov (KS) statistics. Furthermore, CHAID analysis for interaction detection is much more computationally efficient than the stepwise search mentioned above and some identified interactions are shown to have statistically significant predictive power on the target variable. Last but not least, the customer profile created based on the CHAID tree provides a reasonable interpretation of the interactions, which is required by regulations of the credit industry. Hence, this study provides an alternative for handling bankcard classification tasks

A two-stage hybrid model by using artificial neural networks as feature construction algorithms

We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional input variables in logistic regression in the second stage. The model is compared with the traditional one-stage model in credit customer response classification. It is observed that the proposed two-stage model outperforms the one-stage model in terms of accuracy, the area under the ROC curve, and KS statistic. By creating new features with the neural network technique, the underlying nonlinear relationships between variables are identified. Furthermore, by using a very simple neural network structure, the model could overcome the drawbacks of neural networks in terms of its long training time, complex topology, and limited interpretability

An Unexpectedly Broad Thermal and Salinity-Tolerant Estuarine Methanogen Community

Moderately thermophilic (Tmax, ~55 °C) methanogens are identified after extended enrichments from temperate, tropical and low-temperature environments. However, thermophilic methanogens with higher growth temperatures (Topt ≥ 60 °C) are only reported from high-temperature environments. A microcosm-based approach was used to measure the rate of methane production and methanogen community structure over a range of temperatures and salinities in sediment from a temperate estuary. We report short-term incubations (<48 h) revealing methanogens with optimal activity reaching 70 °C in a temperate estuary sediment (in situ temperature 4–5 °C). While 30 °C enrichments amended with acetate, H2 or methanol selected for corresponding mesophilic trophic groups, at 60 °C, only hydrogenotrophs (genus Methanothermobacter) were observed. Since these methanogens are not known to be active under in situ temperatures, we conclude constant dispersal from high temperature habitats. The likely provenance of the thermophilic methanogens was studied by enrichments covering a range of temperatures and salinities. These enrichments indicated that the estuarine sediment hosted methanogens encompassing the global activity envelope of most cultured species. We suggest that estuaries are fascinating sink and source environments for microbial function study

The Chandra Source Catalog

The Chandra Source Catalog (CSC) is a general purpose virtual X-ray astrophysics facility that provides access to a carefully selected set of generally useful quantities for individual X-ray sources, and is designed to satisfy the needs of a broad-based group of scientists, including those who may be less familiar with astronomical data analysis in the X-ray regime. The first release of the CSC includes information about 94,676 distinct X-ray sources detected in a subset of public ACIS imaging observations from roughly the first eight years of the Chandra mission. This release of the catalog includes point and compact sources with observed spatial extents <~ 30''. The catalog (1) provides access to the best estimates of the X-ray source properties for detected sources, with good scientific fidelity, and directly supports scientific analysis using the individual source data; (2) facilitates analysis of a wide range of statistical properties for classes of X-ray sources; and (3) provides efficient access to calibrated observational data and ancillary data products for individual X-ray sources, so that users can perform detailed further analysis using existing tools. The catalog includes real X-ray sources detected with flux estimates that are at least 3 times their estimated 1 sigma uncertainties in at least one energy band, while maintaining the number of spurious sources at a level of <~ 1 false source per field for a 100 ks observation. For each detected source, the CSC provides commonly tabulated quantities, including source position, extent, multi-band fluxes, hardness ratios, and variability statistics, derived from the observations in which the source is detected. In addition to these traditional catalog elements, for each X-ray source the CSC includes an extensive set of file-based data products that can be manipulated interactively.Comment: To appear in The Astrophysical Journal Supplement Series, 53 pages, 27 figure

Statistical Characterization of the Chandra Source Catalog

The first release of the Chandra Source Catalog (CSC) contains ~95,000 X-ray sources in a total area of ~0.75% of the entire sky, using data from ~3,900 separate ACIS observations of a multitude of different types of X-ray sources. In order to maximize the scientific benefit of such a large, heterogeneous data-set, careful characterization of the statistical properties of the catalog, i.e., completeness, sensitivity, false source rate, and accuracy of source properties, is required. Characterization efforts of other, large Chandra catalogs, such as the ChaMP Point Source Catalog (Kim et al. 2007) or the 2 Mega-second Deep Field Surveys (Alexander et al. 2003), while informative, cannot serve this purpose, since the CSC analysis procedures are significantly different and the range of allowable data is much less restrictive. We describe here the characterization process for the CSC. This process includes both a comparison of real CSC results with those of other, deeper Chandra catalogs of the same targets and extensive simulations of blank-sky and point source populations.Comment: To be published in the Astrophysical Journal Supplement Series (Fig. 52 replaced with a version which astro-ph can convert to PDF without issues.

Primary care provider perceptions of intake transition records and shared care with outpatient cardiac rehabilitation programs

Abstract Background While it is recommended that records are kept between primary care providers (PCPs) and specialists during patient transitions from hospital to community care, this communication is not currently standardized. We aimed to assess the transmission of cardiac rehabilitation (CR) program intake transition records to PCPs and to explore PCPs' needs in communication with CR programs and for intake transition record content. Method 144 PCPs of consenting enrollees from 8 regional and urban Ontario CR programs participated in this cross-sectional study. Intake transition records were tracked from the CR program to the PCP's office. Sixty-six PCPs participated in structured telephone interviews. Results Sixty-eight (47.6%) PCPs received a CR intake transition record. Fifty-eight (87.9%) PCPs desired intake transition records, with most wanting it transmitted via fax (n = 52, 78.8%). On a 5-point Likert scale, PCPs strongly agreed that the CR transition record met their needs for providing patient care (4.32 ± 0.61), with 48 (76.2%) reporting that it improved their management of patients' cardiac risk. PCPs rated the following elements as most important to include in an intake transition record: clinical status (4.67 ± 0.64), exercise test results (4.61 ± 0.52), and the proposed patient care plan (4.59 ± 0.71). Conclusions Less than half of intake transition records are reaching PCPs, revealing a large gap in continuity of patient care. PCP responses should be used to develop an evidence-based intake transition record, and procedures should be implemented to ensure high-quality transitional care

Social support and Quality of Life: a cross-sectional study on survivors eight months after the 2008 Wenchuan earthquake

<p>Abstract</p> <p>Background</p> <p>The 2008 Wenchuan earthquake resulted in extensive loss of life and physical and psychological injuries for survivors. This research examines the relationship between social support and health-related quality of life for the earthquake survivors.</p> <p>Methods</p> <p>A multistage cluster sampling strategy was employed to select participants from 11 shelters in nine counties exposed to different degrees of earthquake damage, for a questionnaire survey. The participants were asked to complete the Short Form 36 and the Social Support Rating Scale eight months after the earthquake struck. A total of 1617 participants returned the questionnaires. The quality of life of the survivors (in the four weeks preceding the survey) was compared with that of the general population in the region. Multivariate logistic regression analysis and canonical correlation analysis were performed to determine the association between social support and quality of life.</p> <p>Results</p> <p>The earthquake survivors reported poorer quality of life than the general population, with an average of 4.8% to 19.62% reduction in scores of the SF-36 (p < 0.001). The multivariate logistic regression analysis showed that those with stronger social support were more likely to have better quality of life. The canonical correlation analysis found that there was a discrepancy between actual social support received and perceived social support available, and the magnitude of this discrepancy was inversely related to perceived general health (rs = 0.467), and positively related to mental health (rs = 0.395).</p> <p>Conclusion</p> <p>Social support is associated with quality of life in the survivors of the earthquake. More attention needs to be paid to increasing social support for those with poorer mental health.</p