Methods and applications in the analysis of social data in healthcare
Description: 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:
- Challenges in social data analytics in healthcare:
- data management
- data curation
- 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
- social sensors
- IoT devices
- Applications in social data analytics in healthcare:
- epidemiological analysis
- outbreak detection
- human behavior
- medical skills and education
- personalized medicine
- diagnosis, prognosis and prognostics
The special issue is mainly oriented to the authors that have accepted papers submitted to the IEEE International Symposium on Computer-Based Medical Systems (CBMS 2019). However, other authors which have not participated in the conference can also submit their papers.
- Alejandro Rodríguez González, Universidad Politécnica de Madrid (Spain)
- Sebastian Ventura Soto, Universidad de Córdoba (Spain)
- Paolo Soda, University Campus Bio-Medico di Roma (Italy)
- Jesualdo Tomás Fernández-Breis, Universidad de Murcia, IMIB-Arrixac (Spain)