The growing availability and accessibility of key health-related data resources and the rapid proliferation of technological developments in data analytics is helping to extract the power of these datasets to improve diagnosis, shorten the time to market of drugs, help in early outbreak detection, improve education of healthcare professionals and reduce costs to name but a few.
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.
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 media 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 provides an incredible and rich amount of information that could be analysed and applied to the benefit of public health allowing the quality of life of the population to be improved as well as reducing economic costs. Policymakers, researchers, health professionals and managers are still attempting, with no great success, to acquire health information upon which to base their decisions.
The topics 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
- 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: Call for Papers (main track)
- Please also check the Guidelines.
- Papers must be submitted electronically via the web-based submission system.
- Alejandro Rodríguez-González, Universidad Politécnica de Madrid, Spain
- José Alberto Benítez Andrades, Universidad de León, Spain
- Jose María Alvarez Rodríguez, Carlos III University of Madrid, Spain
- Lucia Prieto Santamaría, Universidad Politécnica de Madrid, Spain
- Ernestina Menasalvas-Ruiz, Universidad Politécnica de Madrid, Spain
A journal special issue will be proposed to authors with accepted papers for submitting an extended version of their work.
Official website: http://sdma.salbis.es/
Easychair platform for submissions: https://easychair.org/conferences/?conf=cbms2021