Intelligent Data Monitoring and Controlling System for Health Related Social Networks

Depression is a worldwide wellbeing concern in view of healthcare. Now a days, social media became popular to allow the affected people to share their experience in the form of posts. These kinds of experiences are stored in the database and extracted and analyzed to give the precautions to the other people or to recall the drugs from the side effects, and other service improvements in their treatment regarding to a particular disease. In such cases depression-related social websites are helpful to monitor or get knowledge in various kinds of drugs, side effects and to share the user experiences. In this paper, we proposed a social media website to allow the users to share the experiences of a particular disease i.e. depression and their experience over on it. We used a weighted network model to represent the activities in the social networks. The proposed work has three steps. The first one is to monitor the user activity and followed by network clustering and the module analysis. The persons who likes a particular post comes under a group and those who contrasted belongs to other group. The stop word technique we have implemented in this work is helpful to avoid the misleading communication over the posts and for the efficient user interaction. The statistical analysis of this kind of user interactions are helpful in health networks to gain much knowledge about a specific disease. This approach will enable all the gatherings to take a part and for the future healthcare improvements to the patients suffering from a disease

A Graph-Based Analysis of Medical Queries of a Swedish Health Care Portal

Today web portals play an increasingly important role in health care allowing information seekers to learn about diseases and treatments, and to administrate their care. Therefore, it is important that the portals are able to support this process as well as possible. In this paper, we study the search logs of a public Swedish health portal to address the questions if health information seeking differs from other types of Internet search and if there is a potential for utilizing network analysis methods in combination with semantic annotation to gain insights into search behaviors. Using a semantic-based method and a graph-based analysis of word cooccurrences in queries, we show there is an overlap among the results indicating a potential role of these types of methods to gain insights and facilitate improved information search. In addition we show that samples, windows of a month, of search logs may be sufficient to obtain similar results as using larger windows. We also show that medical queries share the same structural properties found for other types of information searches, thereby indicating an ability to reuse existing analysis methods for this type of search data