The impacts of nurse-led disease management programs on the quality of life for patients with chronic kidney disease have not been extensively studied. Furthermore, results of the existing related studies are inconsistent. The focus of the proposed meta-analysis is to evaluate the efficacy of nurse-led disease management programs in improving the quality of life for patients with chronic kidney disease. Literature survey was performed to identify the eligible studies from PubMed, Current Nursing and Allied Health Literature, and Cochrane Central Register of Controlled Trials with predefined terms. The outcome measured was quality of life. This meta-analysis was conducted in line with recommendations from the preferred reporting items for systematic reviews and meta-analyses. Eight studies comprising a total of 1520 patients were included in this meta-analysis, with 766 patients assigned to the nurse-led disease management program. Nurse-led disease management improved the quality of life in terms of symptoms, sleep, staff encouragement, pain, general health perception, energy/fatigue, overall health and mental component summary when evaluated 6 weeks after the beginning of intervention. When evaluated 12 weeks later, the quality of life in terms of symptoms, sleep, staff encouragement, energy/fatigue, and physical component summary was improved. Stratified by the modalities of dialysis, similar results of pooled analyses were observed for patients with peritoneal dialysis or hemodialysis, compared with the overall analyses. The results of sensitivity analyses were the same as the primary analyses. The symmetric funnel plot suggested that the possibility of potential publication bias was relatively low. Nurse-led disease management program seems effective to improve some parameters of quality of life for patients with chronic kidney disease. However, the seemingly promising results should be cautiously interpreted and generalized and still need to be confirmed through well-designed large-scale prospective randomized controlled trials.

With the development and popularization of social networks, many users and authoritative media broach and share topics through social networks every day. This type of sharing is widespread and is sometimes the first communication of topics of public discussion. Therefore, research on identifying public opinion topics on social networks is of great significance. However, social network posts are short, sparse and noisy, which creates challenges for this research. To overcome these challenges, we propose a spatial-temporal emergency topic model (ST-ETM) to identify public opinion topics in social networks. By introducing spatial-temporal features into the topic model to construct spatial-temporal regions for focusing on topics, the ST-ETM can alleviate the sparsity problem of context in social networks and succeed in focusing on public opinion topics. Moreover, to automatically identify public opinion topics, we introduce the burstiness of the words as a priori in the model, and binary switch variables are combined to automatically identify public opinion topics in social networks. Based on a real Sina Weibo dataset, several comparative experiments are designed to evaluate the performance of our ST-ETM. The experimental results verify the effectiveness of the proposed ST-ETM method.

Image topic representation in social networks is vital for people to get significant and valuable content. However, this task is difficult and challenging due to the complexity of image features. This paper proposes a multifeature complementary attention mechanism for image topic representation named CATR. CATR uses scene-level and instance-level object detection methods to obtain the object information on social networks. Here, the image features are divided into focused features and unfocused features. Focused features are used to learn and express semantic information, while unfocused features are used to filter out noise information in focused feature extraction. The attention mechanism is constructed by combining the object features and the features of the image itself, while the image topic representation in social networks is realized by the complementary attention mechanism. Based on the real image data of Sina Weibo and Mir-Flickr 25K, several groups of comparative experiments are constructed to verify the performance of the proposed CATR by leveraging different evaluation measures. The experimental results demonstrate that the proposed CATR obtains an optimal accuracy and significantly outperforms the other comparison methods in image topic representation.