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Full Description
In healthcare, a digital twin is a digital representation of a patient or healthcare system using integrated simulations and service data. The digital twin tracks a patient's records, crosschecks them against registered patterns and analyses any diseases or contra indications. The digital twin uses adaptive analytics and algorithms to produce accurate prognoses and suggest appropriate interventions. A digital twin can run various medical scenarios before treatment is initiated on the patient, thus increasing patient safety as well as providing the most appropriate treatments to meet the patient's requirements.
Digital Twin Technologies for Healthcare 4.0 discusses how the concept of the digital twin can be merged with other technologies, such as artificial intelligence (AI), machine learning (ML), big data analytics, IoT and cloud data management, for the improvement of healthcare systems and processes. The book also focuses on the various research perspectives and challenges in implementation of digital twin technology in terms of data analysis, cloud management and data privacy issues.
With chapters on visualisation techniques, prognostics and health management, this book is a must-have for researchers, engineers and IT professionals in healthcare as well as those involved in using digital twin technology, AI, IoT and big data analytics for novel applications.
Contents
Chapter 1: Introduction: digital twin technology in healthcare
Chapter 2: Convergence of Digital Twin, AI, IOT, and machine learning techniques for medical diagnostics
Chapter 3: Application of digital twin technology in model-based systems engineering
Chapter 4: Digital twins in e-health: adoption of technology and challenges in the management of clinical systems
Chapter 5: Digital twin and big data in healthcare systems
Chapter 6: Digital twin data visualization techniques
Chapter 7: Healthcare cyberspace: medical cyber physical system in digital twin
Chapter 8: Cloud security-enabled digital twin in e-healthcare
Chapter 9: Digital twin in prognostics and health management system
Chapter 10: Deep learning in Covid-19 detection and diagnosis using CXR images: challenges and perspectives
Chapter 11: Case study: digital twin in cardiology
