Semantic Models in IoT and eHealth Applications

Semantic Models in IoT and eHealth Applications explores the key role of semantic web modeling in eHealth technologies, including remote monitoring, mobile health, cloud data and biomedical ontologies. The book explores different challenges and issues through the lens of various case studies of healthcare systems currently adopting these technologies. Chapters introduce the concepts of semantic interoperability within a healthcare model setting and explore how semantic representation is key to classifying, analyzing and understanding the massive amounts of biomedical data being generated by connected medical devices. Continuous health monitoring is a strong solution which can provide eHealth services to a community through the use of IoT-based devices that collect sensor data for efficient health diagnosis, monitoring and treatment. All of this collected data needs to be represented in the form of ontologies which are considered the cornerstone of the Semantic Web for knowledge sharing, information integration and information extraction. Presents comprehensive coverage of advances in the application of semantic web in the field of eHealth Explores different challenges and issues through various case studies of healthcare systems that are adopting semantic web technologies Covers applications across a range of eHealth technologies, including remote monitoring and mobile health INDICE: 1. Semantic Modelling for Healthcare Applications: An Introduction2. Role of IoT and semantics in Ehealth3. Evaluation and Visualization of Healthcare Semantic Models4. Role of Connected objects in Healthcare Semantic Models5. The Security and Privacy Aspects in Semantic Web enabled IoT based Healthcare Information Systems6. Knowledge-based System as a Context-aware Approach for the Internet of Medical Connected Objects7. Towards a Knowledge Graph for Medical Diagnosis: Issues and Usage Scenarios8. A Naturopathy Knowledge Graph and Recommendation System to Boost the Immune System9. SAREF4EHAW-Compliant Knowledge Discovery and Reasoning for IoT-based Preventive Healthcare and Well-Being10. Reasoning Over Personalized Healthcare Knowledge Graph: A Case Study of Patients with Allergies and Symptoms11. Integrated Context-aware Ontology for MNCH Decision Support12.  IntelliOntoRec: A Knowledge Infused Semiautomatic Approach for Ontology Formulation in Healthcare and Medical Science

• ISBN: 978-0-323-91773-5
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 380
• Fecha Publicación: 01/07/2022
• Nº Volúmenes: 1
• Idioma: Inglés