Overview

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 a 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 among 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 that 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. This workshop is especially interested in hearing about the challenges and problems data science and AI can address related to the global pandemic, and relevant deployments and experiences in gearing AI to cope with COVID-19. 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)
  • Explainable AI (XAI) in Health and Medical domain
  • Precision Medicine and Health


Format

The workshop will be two full days, consisting of a welcome session, keynote and invited talks, full/short paper presentations, demos, and posters. The organizers have experience hosting virtual conferences and as such have innovative ideas for engaging participants with both presentations and posters.

Submissions

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


Important Dates

November 9, 2020: Submissions due (*** Extended till NOV 13, 2020 ***)
November 30, 2020: Notification of acceptance  (*** Dec 7, 2020 ***)
December 7, 2020: Final Camera-Ready Version  (*** Jan 12, 2021 ***)
February 8, 9, 2021: Workshop


Proceedings

All accepted papers will appear in the workshop proceedings that will be published by Springer/Nature in Studies in Computational Intelligence.


Journal Publication Opportunity

A selected number of papers presented at the workshop will have opportunity to appear in a special issue of a reputable journal in the field, subject to further review. Last years a selected number of revised/extended articles have been published in leading journals in health and biomedical informatics including IEEE Journal of Biomedical and Health Informatics special issue on Explainable AI for Clinical and Population Health Informatics  Artificial Intelligence in Medicine  on "Precision Digital Medicine and Health", and at "Population & Personalized Health Intelligence" Collection in Nature - Digital Medicine.