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Digital Twins: For Superior Clinical Decision Making [Pehme köide]

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  • Formaat: Paperback / softback, 134 pages, kõrgus x laius: 234x156 mm, 3 Tables, black and white; 7 Line drawings, black and white; 7 Illustrations, black and white
  • Sari: Analytics and AI for Healthcare
  • Ilmumisaeg: 22-Aug-2025
  • Kirjastus: CRC Press
  • ISBN-10: 1032780347
  • ISBN-13: 9781032780344
Teised raamatud teemal:
Digital Twins: For Superior Clinical Decision MakingSuurem pilt
  • Formaat: Paperback / softback, 134 pages, kõrgus x laius: 234x156 mm, 3 Tables, black and white; 7 Line drawings, black and white; 7 Illustrations, black and white
  • Sari: Analytics and AI for Healthcare
  • Ilmumisaeg: 22-Aug-2025
  • Kirjastus: CRC Press
  • ISBN-10: 1032780347
  • ISBN-13: 9781032780344
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032780344.html

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.



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.

Arvustused

"For decades, the digital representation of objects and entities has been a crucial field of research in computer science as well as application domains. Dynamic technological progress boosts the relevance by moving from modelling and instantiation towards realistically portrayed digital twins, e.g., by big data and artificial intelligence. Actually, the highest prospective impact of digital twins is anticipated in business and healthcare. Digital twins of products, machines, facilities and human beings will revolutionize the way we create value, for businesses as well as particularly for health providers and patients. This book is an eye-opener to that future."

Freimut Bodendorf, Professor, Friedrich Alexander University, Erlangen, Nuremberg, Germany

"Digital twins are increasingly relevant in the age of personalized medicine. Exploration of the effects of medicines avoids the potential of debilitating side effects and promotes individual health. This book can be expected to have a major impact."

Doug Vogel, PhD, AIS and AIDH Fellow

"This book could not have come at a more opportune time. As providers struggle to raise clinical quality, lower costs, and accomplish this with fewer human resources, technology can help achieve this. Although NASA launched the digital twin effort, the time has come for it to land in healthcare."

Duane F. Wisk, DO, MPH, FACOEM, Adjunct Assistant Professor, Warren Alpert School of Medicine, Brown University

"Digital Twins offers an innovative approach to modern healthcare delivery, where timely decision-making depends on numerous data sources, many of which are available in real time. This book provides a comprehensive overview of Digital Twin applications in healthcare, offering valuable insights for researchers, practitioners, and decision-makers interested in harnessing this technology to enhance healthcare delivery."

Wenny Rahayu, PhD, Professor and Dean of the School of Computing, Engineering and Mathematical Sciences, La Trobe University

Part 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. The Case for Digital
Twins for Healthcare. Part 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 Modelling for Digital Twins in Healthcare. Part
III: The How of Digital Twins.
8. Digital Twins and Clinical Decision-Making.
9. Application of Digital Twins in Healthcare Processes.
10. The Impact of
Blockchain and Digital Twins in the Pharmaceutical Industry.
Nilmini Wickramasinghe is the Optus Chair and Professor of Digital Health at La Trobe University. She has been actively researching and teaching within the health informatics/digital health domain. In 2020, she was awarded an Alexander von Humboldt award for her outstanding contribution to digital health.

Nalika Ulapane is a researcher contributing to the design, development, and assessment of digital health solutions. He brings mathematical modelling, engineering systems design, and design science research principles to solve problems in complex systems like the healthcare sector.

Amir Andargoli focuses primarily on digitalization and digital transformation within the healthcare sector. He draws upon principles from information systems and management to conduct his research, which has resulted in publications in peer-reviewed journals and international symposiums.