Hypertension and its related complications could be a major threat issue for cardiopathy and stroke. Effective prevention and control can decrease the incidence rate of complications in hypertension. Based on the medical data of 3062 patients with cardiovascular and cerebrovascular diseases from 2017 to 2018 in a grade-A tertiary hospital in Shanghai, the study identified the risk factors of hypertension complications by text mining. On this basis, the K2 algorithm based on the improved particle swarm optimization was proposed to optimize the structure of the Bayesian network (BN) by establishing a multi-population cooperative search mechanism. Then the optimized BN was used to analyze and predict the incidence rate of hypertension complications. Results indicate that the major indicators of accuracy, sensitivity, specificity, and AUC have been improved, and the proposed algorithm is superior to the common data mining algorithms such as the back propagation neural network and the decision tree. Through the proposed model and algorithm, the high-risk factors were identified and the occurrence probability of hypertension complications was predicted, which could provide the personalized health management guidance for hypertensive patients to prevent and control hypertension complications.
We review and critique the research literature on sales force automation (SFA). SFA involves the application of information technology to support the sales function. SFA software provides functionality that helps companies manage sales pipelines, track contacts and configure products, inter alia. The paper is organized into four main sections. First, we review the SFA environment, identifying definitions, vendor classifications and software attributes. We then move to a review and classification of the academic research that has been published on SFA. We find that the entire body of SFA knowledge attempts to answer just four questions: Why do organizations adopt SFA? What are the organizational impacts of SFA? What accounts for the success or failure of SFA projects? What accounts for variance in salesperson adoption of SFA? We then critique this body of knowledge on a number of theoretical and methodological grounds, and finally propose a research agenda for the future. 相似文献
In analyzing data from unreplicated factorial designs, the half-normal probability plot is commonly used to screen for the ‘vital few’ effects. Recently, many formal methods have been proposed to overcome the subjectivity of this plot. Lawson (1998) (hereafter denoted as LGB) suggested a hybrid method based on the half-normal probability plot, which is a blend of Lenth (1989) and Loh (1992) method. The method consists of fitting a simple least squares line to the inliers, which are determined by the Lenth method. The effects exceeding the prediction limits based on the fitted line are candidates for the vital few effects. To improve the accuracy of partitioning the effects into inliers and outliers, we propose a modified LGB method (hereafter denoted as the Mod_LGB method), in which more outliers can be classified by using both the Carling’s modification of the box plot (Carling, 2000) and Lenth method. If no outlier exists or there is a wide range in the inliers as determined by the Lenth method, more outliers can be found by the Carling method. A simulation study is conducted in unreplicated designs with the number of active effects ranging from 1 to 6 to compare the efficiency of the Lenth method, original LGB methods, and the proposed modified version of the LGB method. 相似文献
