The Second IEEE SERVICES Workshop on Big Data for public health policy making
As acknowledged by the World Health Organization, by a number of governmental institutions, and patient associations, the effective management of health-related problems depends on and requires appropriate public health policies. A public health policy may have a significant effect on the prevention and early diagnosis and early treatment of several diffuse and debilitating conditions including, for instance, cognitive decline. On the other hand, health policies can be also focused to target personalized medicine and home healthcare, exploiting the knowledge obtained from the collectivity tuning it for a specific situation.
The management of such health problems and their consequences through public health policies can benefit from the analysis of heterogeneous data (e.g., collected from modern IoT sensors as well as from standard clinical trials).
Big Data Analytic techniques enable the investigation of specific health problems, their possible relations to other comorbidities and contextual factors, and patterns of such relations.
The workshop seeks submissions from academia and industry presenting novel research on all theoretical and practical aspects concerning the adoption of Big Data Analytics in the context of evidence-based public health policy making. The workshop targets innovative techniques and solutions for supporting policy makers and clinicians in: i) taking decisions based on evidence and on ii) simulating scenarios aimed at predicting evolutions of health diseases at epidemiological level. It also aims to investigate security and privacy concerns related to the analysis of health data and the possible impact that security and privacy measures may produce on the achievable quality of analyses and the effectiveness of health policies. The workshop aims also to investigate: i) the modelling and adoption of advanced Artificial Intelligence models for policy making based on simulations and open data and ii) the possible trade-off between the design and implementation of health policies that may require years to produce their expected results and solutions able to rapidly produce tangible results.
The workshop will bring together researchers of different disciplines, policy makers and clinicians, from academia and industry, all sharing a common goal: to go beyond the frontier of today’s public health policy making process by envisioning how to exploit the full potential of Big Data Analytics in ways compliant with the principles and needs of modern societies, like satisfying security and privacy requirements.
Topics for the workshop include, but are not limited to:
- Data driven public health policy making models
- Model based Big data solutions for health-related policy making
- Decision support systems for clinicals and policy making
- Innovative Big Data as a service architecture for health
- Security aspects of Big Data analytics threating sensible data
- Privacy aware Big Data models and analytics in health scenarios
- AI models for health-related policy making
- Mixing simulations and open data for health policies predictions
- Advanced reaction-oriented policy models for health
- IOT sensing capabilities for health
- IOT sensing and policies for personalized medicine
Marco Anisetti, Università degli Studi di Milano, Italy
George Spanoudakis, City University of London, UK
We call for original and unpublished papers no longer than 6 pages (up to 2 additional pages may be purchased subject to approval by the Publication Chair.). All papers will be reviewed with a minimum of 3 good-quality reviews per paper. The manuscripts should be formatted in standard IEEE camera-ready format (double-column, 10-pt font) and be submitted as PDF files (formatted for 8.5×11-inch paper). The submission URL is:
Authors wishing to submit a paper to this workshop must select the track entitled “IEEE SERVICES Workshop on Big Data for public health policy making” in order to be considered.
For general questions about this workshop, please contact either workshop organizer: