Special Issue on Social data analytics for public health

Journal (JCR 2015: 0.887): Computational and Mathematical Methods in Medicine

Public health is an interdisciplinary field encompassing a wide range of subject areas (medicine, science, epidemiology, public and socialaffairs, etc.). 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, socialnetworks, etc.). In particular, social environments are a new source of data that allows information to be obtained at all community levels (from physicians to patients).

Public health would benefit directly through the analysis of the information generated in any kind of social environment such as socialnetworks, 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 healthallowing the quality of life of the population to be improved as well as reducing economic costs. Policymakers, researchers, healthprofessionals and managers are still attempting, with no great success, to acquire health information upon which to base their decisions.

The topics to be covered include, but not limited to:

Challenges in social data analytics for public health: i) data management; ii) data curation, iii) opinion mining and sentiment analysis; iv) privacy-aware data mining algorithms; v) data quality and veracity; vi) natural language processing and text-mining; vii) semantics; viii) trend discovery and analysis; ix) graph mining and community detection, x) social sensors, xi) IoT devices.

Applications in social data analytics for public health: i) epidemiological analysis; ii) outbreak detection; iii) human behaviour; iv) medical skills and education; v) personalized medicine; vi) diagnosis, prognosis and prognostics.

Reviews of any of the aforementioned topics.

Application domains of interest include finance, open innovation, healthcare, digital libraries, organizational learning, knowledge management, e-government, eLearning, decision support, and multimedia systems.

Important Dates:

Full initial paper submission deadline: November 25, 2016
First Review deadline: February 17, 2017
Publication date: April 14, 2017

Submission instructions and more:

http://www.personal.fi.upm.es/~alejandrorg/pubs/si/cmmm.html