This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.

BackgroundFew studies of laparoscopic cholecystectomy (LC) outcome have used longitudinal data for more than two years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility) of the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR) and multiple linear regression (MLR) models has not been adequately addressed. This study proposed to validate the use of these models for predicting quality of life (QOL) after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR.Methodology/Principal FindingsA total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for predicting QOL after LC.Conclusions/SignificanceCompared with SVM, GPR and MLR models, the ANN model in this study was more accurate in predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

Purpose The purpose of this paper is to develop a deeper understanding of how to promote members’ beneficial behaviors toward other members and toward the virtual community (VC). The authors extend Ray et al.’s (2014) framework by developing a more precise definition of community embeddedness, and determining how such embeddedness relates to social support and community engagement. Design/methodology/approach The authors test the proposed research model using data collected from 333 users of online social support communities/groups dedicated to sharing knowledge about pregnancy and child care. Partial least squares is used to analyze the measurement and structural models. Findings The study shows that embeddedness and engagement are significant determinants of willingness to help others and willingness to help the community. Embeddedness has a strong, positive effect on engagement. Social support positively affects community identification and embeddedness. However, community identification does not have a significant effect on engagement. Research limitations/implications Some of the findings, such as the relative importance of embeddedness in fostering willingness to help the community and the relative importance of engagement in fostering willingness to help others, might not be generalizable to VCs where members join for fun and sharing interests. Practical implications Although knowledge contributors could self-derive some drivers of embeddedness and engagement, managers or hosts of VCs should develop strategies and mechanisms to provide or enhance the value they add to knowledge sharing and other beneficial behaviors, even though such added value might be largely intangible. Social implications Social support plays an important role in shaping an individual’s embeddedness within a VC. Managers of VCs should develop strategies to stimulate exchanges of support among members. Originality/value The authors believe that community embeddedness plays a more important role than engagement in shaping the VC’s success and effectiveness. However, the extant VC literature has indicated a relatively weak understanding of the notion of community embeddedness. This study intends to fill that void.