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. 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)
- Hybrid Methods, combining data driven and predictive forward models
- Explainable AI (XAI) in Health and Medical domain
- Precision Medicine and Health
- Response to Covid-19
Format
The workshop will be consisting of a welcome session, keynote and invited talks, full/short paper presentations, demos, posters, a panel discussion, and dissemination of results from the hackathon. We will propose a joint session with IAAI-22 to disseminate the best results from the hack-a-thon (Dr. Michalowski is the Outreach Chair for IAAI-22 and will facilitate the collaboration).
Submissions
We invite workshop participants to submit their original contributions (Single Blind) 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. Participants in the hack-a-thon will be asked to either register as team or be randomly assigned to a team after registration. Their results will be submitted in either a short paper or poster format. A dataset(s) will be provided to hack-a-thon participants.
Important Dates
November 12, 2021: Submissions due (*** Extended till NOV 19, 2021 ***)
December 3, 2021: Notification of acceptance (*** Extended till Dec 15, 2021 ***)
December 17, 2021: Final Camera-Ready Version (*** Extended till Jan 10, 2022 ***)
February 28 and March 1, 2022: W3PHIAI'22 Workshop Program
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 Experimental Biology and Medicine (EBM) , 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.