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.
1. Chapter 1 : Semantic modeling for healthcare applications: an introduction - p. Page x 2. Chapter 2 : Role of IoT and semantics in e-Health - p. Page 14 3. Chapter 3 : Evaluation and visualization of healthcare semantic models - p. Page 36 4. Chapter 4 : Role of connected objects in healthcare semantic models - p. Page 65 5. Chapter 5 : The security and privacy aspects in semantic web enabled IoT-based healthcare information systems - p. Page 84 6. Chapter 6 : Knowledge-based system as a context-aware approach for the Internet of medical connected objects - p. Page 110 7. Chapter 7 : Toward a knowledge graph for medical diagnosis: issues and usage scenarios - p. Page 126 8. Chapter 8 : A naturopathy knowledge graph and recommendation system to boost the immune system: KISS: Knowledge-based Immune System Suggestion - p. Page 141 9. Chapter 9 : SAREF4EHAW-compliant knowledge discovery and reasoning for IoT-based preventive health and well-being: IoT-based preventive health and well-being knowledge discovery and reasoning - p. Page 166 10. Chapter 10 : Reasoning over personalized healthcare knowledge graph: a case study of patients with allergies and symptoms - p. Page 195 11. Chapter 11 : Integrated context-aware ontology for MNCH decision support - p. Page 223 12. Chapter 12 : IntelliOntoRec: a knowledge infused semiautomatic approach for ontology formulation in healthcare and medical science - p. Page 240 13. Index - p. Page 263
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.
1. Chapter 1 : Semantic modeling for healthcare applications: an introduction - p. Page x 2. Chapter 2 : Role of IoT and semantics in e-Health - p. Page 14 3. Chapter 3 : Evaluation and visualization of healthcare semantic models - p. Page 36 4. Chapter 4 : Role of connected objects in healthcare semantic models - p. Page 65 5. Chapter 5 : The security and privacy aspects in semantic web enabled IoT-based healthcare information systems - p. Page 84 6. Chapter 6 : Knowledge-based system as a context-aware approach for the Internet of medical connected objects - p. Page 110 7. Chapter 7 : Toward a knowledge graph for medical diagnosis: issues and usage scenarios - p. Page 126 8. Chapter 8 : A naturopathy knowledge graph and recommendation system to boost the immune system: KISS: Knowledge-based Immune System Suggestion - p. Page 141 9. Chapter 9 : SAREF4EHAW-compliant knowledge discovery and reasoning for IoT-based preventive health and well-being: IoT-based preventive health and well-being knowledge discovery and reasoning - p. Page 166 10. Chapter 10 : Reasoning over personalized healthcare knowledge graph: a case study of patients with allergies and symptoms - p. Page 195 11. Chapter 11 : Integrated context-aware ontology for MNCH decision support - p. Page 223 12. Chapter 12 : IntelliOntoRec: a knowledge infused semiautomatic approach for ontology formulation in healthcare and medical science - p. Page 240 13. Index - p. Page 263
