دانلود کتاب Digital Twin Technologies for Healthcare 4.0

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.

In the recent years, Digital Twin (DT) has gained a remarkable place in the top ten technology trends. But digital twinning alone cannot provide solution to many applications. Hence, the integration of DT with the technologies like Internet of Things (IoT), Artificial Intelligence (AI), Cloud Computing, Machine Learning (ML), Big Data analytics, and Deep Learning (DL) techniques paves way to new opportunities and provides a solution to many research problems in diverse sectors. The DT provides an accumulation of data between the real and digital system in both paths. This chapter focuses on the definition, architecture, components, and different types of DT. It also emphasizes on the convergence of digital twinning with other technologies for solving many research problems and application areas in medical diagnosis, healthcare, and others. It also highlights on the issues and challenges pertaining to DT and its supporting technologies. Finally, a case study pertaining to manufacturing sector using DT is presented.

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.