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The work 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




The identification of biomarkers to describe, define and predict biological age is a very active topic of research. Aging affects organisms differently, and chronological age does not always coincide with biological age, a statement all the more true for diseases like progeroid syndromes and other accelerated aging conditions. Having reliable predictors of chronological age, biological age, and their relationship is important for both diagnostic and prognostic uses (e.g., for disease co-morbidity and mortality), as well as research and clinical use (e.g., rejuvenation and reprogramming, therapeutic outcome, etc.). Current age indicators rely on molecular and genetic data, usually for biological age, like methylation data and telomere length, mobile phone data, X-ray scans, and face images for chronological age. 


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