W3PHI 2016 - 3rd AAAI International Workshop on the World Wide Web and Population Health Intelligence (W3PHI 2016)

Severe pandemics due to highly‐transmissible viruses continue to threaten the world in the 21st century. In a tightly interconnected world, infectious disease outbreaks can adversely affect economic growth, trade, tourism, business and industry, and social stability as well as public and population health. Public health authorities and researchers now collect data from many sources, and analyze these data together to estimate the incidence and prevalence of different health conditions, as well as related risk factors. Modern surveillance systems employ tools and techniques from artificial intelligence and machine learning to monitor direct and indirect signals and indicators of disease activities for early, automatic detection of emerging outbreaks and other health-relevant patterns.
Tracking of internet-based health indicators complements other surveillance methods collecting data from clinical systems and registries. To provide proper alerts and timely response public health officials and researchers systematically gather news, and other reports about suspected disease outbreaks, bioterrorism, and other events of potential international public health concern, from a wide range of formal and informal sources. With the advent of modern communication technologies, many outbreak reports now originate in electronic media and electronic discussion groups. Given the ever increasing role of the World Wide Web as a source of information in many domains including healthcare, accessing, managing, and analyzing its content has brought new opportunities and challenges. This is especially the case for non-traditional online resources such as social networks, blogs, news feed, twitter posts, and online communities with the sheer size and ever-increasing growth and change rate of their data. Web applications along with text processing programs are increasingly being used to harness online data and information to discover meaningful patterns identifying emerging health threats. The advances in web science and technology for data management, integration, mining, classification, filtering and visualization has given rise to variety of applications representing real-time data on epidemics.
Topics
The workshop will include original contributions on theory, methods, systems, and applications of data mining, machine learning, databases, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based healthcare applications, with special focus on public health. The scope of the workshop includes, but not limited, the following areas:
Geographical Mapping and Visual analytics for Health Data
Social Media Analytics
Epidemic Intelligence
Predictive modelling and Decision support
Biomedical Ontologies, terminologies and standard
Bayesian Networks and Reasoning under Uncertainty
Temporal and Spatial Representation and Reasoning
Case-based Reasoning in Healthcare
Crowdsourcing, and Collective Intelligence
Risk assessment, Trust, Ethics, and Privacy
Sentiment Analysis and Opinion Mining
Computational Behavioral/Cognitive Modeling
Applications in Epidemiology and Surveillance (e.g. Bioterrorism, Participatory Surveillance)
Following the success of AAAI-W3PHI 2014 (Quebec City) and AAAI-W3PHI 2015 (Austin, TX), this workshop aims to bring together a wide range of computer scientists, researchers, students, industry professionals, national and international public health agencies, and NGOs interested in the theory and practice of computational models of web-based population health intelligence to highlight the latest achievements in epidemiological surveillance based on monitoring online communications and interactions on the World Wide Web. The workshop promotes open debate and exchange of opinions among participants.