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Full Description
This book centres on the topic of digital twins for superior healthcare decision support, as access is enabled to large volumes of multi-dimensional data such as patient's electronic medical records, medical scans, and data. The reader learns about the possibility of a digital representation of analogous clinical cases built from data-driven models to represent and present relevant information and germane knowledge in context.
Together with cutting-edge technologies, authors share the ability of data-driven models to offer more efficient clinical decision support. The authors take a three-prong approach in the study of digital twins, the positive contributions made in other industries, the different types of applications, and the numerous benefits offered. Artificial Intelligence (AI) techniques, such as Machine Learning (ML) and Deep Learning (DL) algorithms are discussed in the context of digital twins in healthcare applications. By looking at how digital twins reduce workflow challenges, provide fast and precise diagnosis, therefore support superior clinical decision making. Importantly, the editors identify critical success issues including co-design and research, for the design, development, and deployment of suitable digital twins.
This book is written for the healthcare audience, professionals, physicians, medical administrators, managers, and the IT practitioner. It would also serve as a useful reference for the senior level undergraduate students and graduate students in health informatics and public health.
Contents
I THE WHY OF DIGITAL TWINS / WHY NOW 1 Decision making in healthcare and the rise of technology and the impact of the digital transformation 2 Digital twins in other industries 3 Case for digital twins for healthcare II THE WHAT OF DIGITAL TWINS 4 From Algorithms to Outcomes: Leveraging Machine Learning Clustering Techniques for Enhanced Clinical Decision Support 5 Clinical Decision Support through Federated Learning and Blockchain 6 From Algorithms to Outcomes: Leveraging Machine Learning Classification Techniques for Enhanced Clinical Decision Support 7 From Perceptron to Liquid Neural Networks: The Evolution of Neural Networks and Their Role in Black Box Modeling for Digital Twins III THE HOW OF DIGITAL TWINS 8 DT for Clinical decision making 9 DT for process flow / workflow enhancement 10 DT for pharma and bio tech. Epilogue - Bringing it all together - DT and value-based care that is precise and personalised
