Health information systems (HIS) play a crucial role in healthcare in the 21st Century. Biomedical ontologies and controlled vocabularies provide structured domain knowledge to a variety of health information systems to support interoperability, electronic health records, healthcare administration, and clinical decision support. The rich thesaurus with concepts linked by semantic relationships has been widely used in natural language processing, data mining, machine learning, semantic annotation, and automated reasoning. The dramatically increasing amount of healthcare data poses unprecedented opportunities for mining previously unknown knowledge with semantics-powered data analytics methods. However, due to the complexity of heterogeneous health information systems and the lack of interoperability among them, it is a challenging problem to exploit multiple data sources to solve real world problems such as predicting disease progression, designing personalized treatment plan, and identifying patient cohort for clinical trials.

This special issue aims to bring people in the field of knowledge representation, knowledge management, and health data analytics to introduce innovative semantics-based methods to address important problems in healthcare engineering with biomedical, clinical, behavioral, and social web data. We invite authors to contribute original research articles as well as review articles that will illustrate the use of biomedical ontologies and semantic web technologies to facilitate healthcare engineering and discover hidden knowledge in biomedical and health data.

Potential topics include but are not limited to the following:

• Semantics-based healthcare data analytics
  • Ontology-based healthcare data mining
  • Ontology-based analysis on biomedical, clinical, or social web data
  • Named entity recognition and relation extraction on biomedical, clinical, or social web data
  • Data mining or machine learning on biomedical, clinical, or social web data
  • Semantic annotation on biomedical, clinical, or social web data
• Application of ontologies in healthcare engineering
  • Applications of ontologies in health information systems
  • Ontology-driven software framework for healthcare applications
  • Ontology-based health data interpretation and visualization
  • Algorithmic phenotyping and cohort identification using ontologies
  • Health consumers oriented tools for information seeking
• Health data integration
  • Linked open data in healthcare engineering
  • Novel approaches for data integration of heterogeneous healthcare data sources
• Ontologies and controlled terminologies
  • Ontology development and enrichment for healthcare applications
  • Quality assurance of biomedical ontologies and terminologies
  • Semantic harmonization and ontology alignment for health information systems
  • Knowledge representation and reasoning