Methods and applications in the analysis of social data in healthcare
• 大类 : 工程技术 - 4区
• 小类 : 计算机：信息系统 - 1区
The growing availability and accessibility of diverse and relevant health-related data resources, and the rapid proliferation of technological developments in data analytics is contributing to make the most of extracting the power of these datasets, to improve diagnosis and decision making, shorten the development of new drugs from discovery to marketing approval, facilitate early outbreak detection, improve healthcare professionals training and reduce costs to name but a few examples.
Extracting the knowledge to make this a reality is still a daunting task: on the one hand, data sources are not integrated, they contain private information and are not structured. On the other hand, we still lack context- and privacy-aware algorithms to extract the knowledge after a proper curation and enrichment of the datasets.
In recent years technology has made it possible not only to get data from many healthcare settings (hospitals, primary care centers, laboratories, etc.), it also allows information to be obtained from the society itself (sensors, Internet of Things (IoT) devices, social networks, etc.). For instance, social media environments are a new source of data coming from all the community levels.
For this reason, the organization of the current special issue responds to the necessity in collecting the last efforts that have been made in these areas of research. The special issue aims to publish high-quality research papers focused on the analytics of social data related to healthcare as well as those studies and works that include the processes needed to perform such analytics.
The topics to be covered include, but are not limited to:
1. Challenges in social data analytics in healthcare:
opinion mining and sentiment analysis
privacy-aware data mining algorithms
data quality and veracity
natural language processing and text mining
trends in discovery and analysis
graph mining and community detection
2. Applications in social data analytics in healthcare: