Social data and medical data analytics special track

The growing availability and accessibility of key health-related data resources and the rapid proliferation of technological developments in data analytics are supposing a revolution in health care. The power of these datasets allows improving diagnosis, shortening drugs’ time to market, helping in early outbreak detection, improving education of healthcare professionals and reducing costs to name but a few.

Extracting the knowledge to make this a reality is still a daunting endeavour: 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.

Technology in recent years has made it possible not only to get data from the healthcare environment (hospitals, health centres, laboratories, etc.). It also allows information to be obtained from society itself (sensors, monitoring, Internet of Things (IoT) devices, social networks, etc.). In particular, social environments are a new source of data that allows information to be obtained at all community levels.

Health environments would benefit directly through the acquisition and the analysis of the information generated in any kind of social environment such as social networks, forums, chats, social sensors, Internet of Things (IoT) devices, surveillance systems, virtual worlds, to name but a few. These environments provide an incredible and rich amount of information that could be analysed and applied to the benefit of public health, allowing improving the quality of life of the population as well as reducing economic costs. Policymakers, researchers, health professionals and managers are still attempting, with limited success, to acquire health information upon which to base their decisions.

The topics of interest include, but are not limited to:

Challenges in social data analytics

• data management
• data curation
• opinion mining and sentiment analysis
• privacy-aware data mining algorithms
• data quality and veracity
• natural language processing and text-mining
• semantics
• trend discovery and analysis
• graph mining and community detection
• social sensors
• IoT devices

Applications in social data analytics

• epidemiological analysis
• outbreak detection
• human behaviour
• medical skills and education
• personalized medicine
• diagnosis, prognosis and prognostics

• Prospective authors are invited to submit papers in any of the topics listed above.
• Instructions for preparing the manuscript (in Word and Latex formats) are available at: Paper Templates
• Please also check the Guidelines.
• Papers must be submitted electronically via the web-based submission system using the appropriated button on this page.

• Massimiliano Zanin, PhD (Center for Biomedical Technology, Universidad Politécnica de Madrid, Spain)
• Athena Vakali, PhD (Aristotle University, Greece)
• Jose Alberto Benitez Andrades, PhD (Universidad de León, Spain)

Check track webpage!