Disruptive technologies are gaining importance in healthcare systems and health informatics. By discussing computational intelligence, IoT, blockchain, cloud and big data analytics, this book provides support to researchers and other stakeholders involved in designing intelligent systems used in healthcare, its products, and its services.
This book offers both theoretical and practical application-based chapters and presents novel technical studies on designing intelligent healthcare systems, products, and services. It offers conceptual and visionary content comprising hypothetical and speculative scenarios and will also include recently developed disruptive holistic techniques in healthcare and the monitoring of physiological data. Metaheuristic computational intelligence-based algorithms for analysis, diagnosis, and prevention of disease through disruptive technologies are also provided.
Designing Intelligent Healthcare Systems, Products, and Services Using Disruptive Technologies and Health Informatics is written for researchers, academicians, and professionals to bring them up to speed on current research endeavours, as well as to introduce hypothetical and speculative scenarios.
1. Telemedicine (e-Health, m-Health): Requirements, Challenges and Applications.
2. Future Risk Analysis of the Health Public Sector During COVID-19 Period (2020 to March 2021).
3. Role of Advanced Technologies in Gait Analysis and Its Importance in Healthcare.
4. Emerging Disruptive Technologies and Their Impact on Health Informatics.
5. Scaling Up Telemedicine in India: Moving Towards Intelligent Healthcare via Disruptions.
6. A Wearable ECG Sensor for Intelligent Cardiovascular Health Informatics.
7. Recent Trends in Wearable Technologies, Challenges and Opportunities.
8. Intelligent Depression Detection System Using Effective Hyper-Scanning Techniques.
9. Design of an Intelligent System for Diabetes Prediction by Integrating Rough Set Theory and Genetic Algorithm.
10. Blockchain for the Healthcare Sector: Application and Challenges.
11. Blockchain-Enabled Secured Medical Supply Chain Management.
12. Big Data in Healthcare: Technological Implications and Challenges.
13. An Efficient System for Predictive Analysis on Brain Cancer Using Machine Learning and Deep Learning Techniques.
14. A Review Study on Different Machine Learning Algorithms Used for COVID Outbreak Prediction.
15. Designing a Rough-PSO-Based COVID-19 Prediction Model.
16. Transitions in Machine Learning Approaches for Healthcare-Sector Applications.