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.