In conjunction with the 31th AAAI Conference on Artificial Intelligence (AAAI-17) on Feb 4–5, in San Francisco, California, USA

Public health authorities and researchers 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. 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. 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.

Moreover, to tackle and overcome several issues in personalized healthcare, information technology will need to evolve to improve communication, collaboration, and teamwork between patients, their families, healthcare communities, and care teams involving practitioners from different fields and specialties. All of these changes require novel solutions and the AI community is well positioned to provide both theoretical- and application-based methods and frameworks. The goal of this workshop is to focus on creating and refining AI-based approaches that (1) process personalized data, (2) help patients (and families) participate in the care process, (3) improve patient participation, (4) help physicians utilize this participation in order to provide high quality and efficient personalized care, and (5) connect patients with information beyond those available within their care setting. The extraction, representation, and sharing of health data, patient preference elicitation, personalization of “generic” therapy plans, adaptation to care environments and available health expertise, and making medical information accessible to patients are some of the relevant problems in need of AI-based solutions.

The workshop will include original contributions on theory, methods, systems, and applications of data mining, machine learning, databases, network theory, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based healthcare applications, with a focus on applications in population and personalized health. The scope of the workshop includes, but is not limited to, the following areas:

The workshop will be two full days consist of welcome session, keynote and invited talks, full/short paper presentations, demos, posters, and one or two panel discussion.

We invite researchers and industrial practitioners to submit their original contributions following the AAAI format through EasyChair . Three categories of contribution are sought: full-research papers up to 8 pages; short paper up to 4 pages; and posters and demos up to 2 pages.

Research papers should demonstrate a novel computational theories or methodologies for addressing issues in health and healthcare. Papers discussing systems and applications should explain the development, implementation or evaluation of innovative tools and systems with potential benefits to solve problems in healthcare. Short papers should report works-in-progress, system descriptions, qualified opinions, positions, or recommendations on emerging topics.

Each paper will be reviewed by at least three reviewers from the program committee. The submitted research papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing.

Proceedings

All accepted papers will appear in the AAAI 2017 Workshops Proceedings. We will tentatively invite the best submissions to extend their papers in order to be published in a special issue of a journal. Also the post-workshop proceedings including the extended/revised versions of selected papers will be published in a volume in Lecture Notes in Social Networks (Springer).