Abstract:
The COVID-19 pandemic has catapulted healthcare research as a top priority for many nations. Researchers have used data-driven approaches to better understand COVID-19, develop effective vaccines, and mitigate the spread of the virus. Healthcare data management continues to evolve as mankind faces the biggest public health crisis of modern times. As new kinds of data emerge, new models, algorithms, and techniques are needed to better harness the value of healthcare data for advanced decision making.
On the other hand, Semantic Web technologies can provide effective solutions for enabling interoperability and common language among healthcare systems, and can lead to the disambiguation of the information through the adoption of various terminologies and ontologies available. In addition, AI and machine learning can enable data-driven decision making and extracting meaningful insights from complex healthcare datasets. Thus, knowledge representation and reasoning on healthcare data become even more important. Semantic Web technologies have matured over the years and can provide these capabilities by design.
The purpose of this Special Issue is to collect contributions on the cross-cutting the fields of Semantic Web, data science, data management, and health informatics to discuss the challenges in healthcare data management and to propose novel and practical solutions for the next generation of data-driven healthcare systems. The ultimate goal is to enable new innovations in Semantic Web, knowledge management, and data management for healthcare systems to move the needle to achieve the vision of precision medicine.
After two rigorous review rounds, 6 papers have been accepted for publication in this Semantic Web – Interoperability, Usability, Applicability special issue.
