**** The 1st Joint Workshop on Health Intelligence W3PHIAI (W3PHI & HIAI)
http://w3phiai2017.w3phi.com/index.html
In conjunction with the 31th AAAI Conference on Artificial Intelligence (AAAI-17) on Feb 4–5, in San Francisco, California, USA
This two-days workshop will address various aspects of using AI for improving population and personalized healthcare, and is structured in two tracks focusing on population (W3PHI) and personalized health (HIAI). Following the success of AAAI-W3PHI 2014 –16 and AAAI-HIAI 2013 – 16 this workshop aims to bring together a wide range of computer scientists, clinical and health informaticians, researchers, students, industry professionals, national and international health and public health agencies, and NGOs interested in the theory and practice of computational models of population health intelligence and personalized healthcare to highlight the latest achievements in the field. The workshop promotes open debate and exchange of opinions among participants.
*** Workshop Description
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.
*** Topics
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:
• Knowledge Representation and Extraction
• Integrated Health Information Systems
• Patient Education
• Patient-Focused Workflows
• Shared Decision Making
• Geographical Mapping and Visual Analytics for Health Data
• Social Media Analytics
• Epidemic Intelligence
• Predictive Modeling and Decision Support
• Semantic Web and Web Services
• Biomedical Ontologies, Terminologies and Standards
• 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, Privacy, and Security
• Sentiment Analysis and Opinion Mining
• Computational Behavioral/Cognitive Modeling
• Health Intervention Design, Modeling and Evaluation
• Online Health Education and E-learning
• Mobile Web Interfaces and Applications
• Applications in Epidemiology and Surveillance (e.g. Bioterrorism, Participatory Surveillance, Syndromic Surveillance, Population Screening)
*** Format
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.
*** Submission requirements
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.
Workshop Organizing Committee
Arash Shaban-Nejad, PhD. (Co-chair)
The University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics,
Department of Pediatrics,
50 N Dunlap Ave, Memphis, TN 38103, USA
(901) 287-5823 (tel),
email: ashabann@uthsc.edu
url:https://academic.uthsc.edu/faculty/facepage.php?netID=ashabann&personnel_id=343203
Martin Michalowski, PhD. (Co-chair)
email: martin.michalowski@gmail.com
David L. Buckeridge, MD, PhD.
McGill Clinical & Health Informatics,
McGill University
1140 Pine Avenue West,
Montreal, Quebec, H3A 1A3 CANADA,
(514) 398-8355 (tel)
(514) 843-1551 (fax)
email: david.buckeridge@mcgill.ca
url: http://surveillance.mcgill.ca/
John S. Brownstein, PhD.
Boston Children’s Hospital,
Harvard University,
Autumn St, Room 451,
Boston, MA 02215 USA,
(617) 355-6998 (tel)
(617) 730-0921 (fax)
email: john_brownstein@harvard.edu
url: http://chip.org/john-brownstein
Byron C. Wallace, PhD
College of Computer and Information Science,
Northeastern University,
360 Huntington Ave. Boston, Massachusetts, Boston, MA 02115, USA
email: byron@ccs.neu.edu
url: http://byronwallace.com
Michael J. Paul, Ph.D.
Department of Information Science,
University of Colorado Boulder,
Environmental Design 201315 UCB,
1060 18th Street Boulder, CO 80309, USA
email: mpaul@colorado.edu
url: http://cmci.colorado.edu/~mpaul/
Szymon Wilk, Ph.D.
Division of Intelligent Decision Support Systems,
Faculty of Computing Science,
Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
email: szymon.wilk@cs.put.poznan.pl
*** Workshop Scientific Committee:
Mark Musen, Stanford University, USA
Senjuti Basu Roy, University of Washington, Tacoma, USA
Nigel Collier, University of Cambridge, UK
David L. Buckeridge, McGill University, Canada
Maged Kamel Boulos, University of Highlands and Islands, UK
John S. Brownstein, Harvard University, USA
Jiang Guoqian, Mayo Clinic, Rochester, USA
Neil F. Abernethy, University of Washington, USA
Christopher J.O. Baker, University of New Brunswick, Canada
Alessio Signorini, University of Iowa, USA
Arash Shaban-Nejad, University of Tennessee Health Sci. Center, USA
Jason J. Jung, Chung-Ang University, Republic of Korea
Courtney D. Corley, Pacific Northwest National Lab, USA
Anette Hulth, Public Health Agency of Sweden, Sweden
Trevor Cohen, University of Texas Health Science, USA
Michael J. Paul, University of Colorado Boulder, USA
Noémie Elhadad, Columbia University, USA
Yan Zhang, University of Texas at Austin, USA
Ian Painter, University of Washington, USA
Christopher C. Yang, Drexel University, USA
Martin Michalowski, USA
Byron C. Wallace, Northeastern University, USA
Simone Bianco, IBM Research, Almaden, USA
Jenna Wiens, University of Michigan. USA
Liqiang Nie, National University of Singapore, Singapore
Sabine Bergler, Concordia University, Canada
Rumi Chunara, New York University, USA
José Luis Ambite ,University of Southern California, USA
Yao-Yi Chiang , University of Southern California, USA
Aniko Ekart, Aston University, UK
Dan Goldberg , Texas A&M University, USA
Arjen Hommersom, University of Nijmegen, Netherlands
Andrey Kolobov, Microsoft Research, USA
Matt Michelson, InferLink, USA
Jeremy Weiss, Carnegie Mellon University, USA
Craig Kuziemsky, University of Ottawa, Canada
Leandro Marcolino, Lancaster University, UK
Stan Matwin, Dalhousie University, Canada
Dympna O’Sullivan, City University London, UK
Niels Peek, The University of Manchester, UK
Jerzy Stefanowski, Poznan University of Technology, Poland
Xing Tan, York University, Canada
Yuanlin Zhang, Texas Tech University, USA
Jesse Davis, Katholieke Universiteit Leuven, Belgium
Mihaela van der Schaar, University of California, Los Angeles, USA
Eun Kyong Shin, University of Tennessee Health Sci. Center, USA
*** Important Dates
October 31: Submissions due
November 20: Notification of acceptance
December 8: Camera-ready copy due to AAAI
February 4-5: W3PHIAI’17 Workshop Program
*** 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).
Workshop URL: http://w3phiai2017.w3phi.com/index.html