Digital Twins - For Superior Clinical Decision Making

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.

List of 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

About the author

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 primary focus is 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.

Product details

• Authors: Amir Andargoli, Nalika Ulapane, Nilmini Ulapane Wickramasinghe, Wickramasinghe Nilmini
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Release: 28.07.2025
• EAN: 9781032780351
• ISBN: 978-1-0-3278035-1
• No. of pages: 138
• Series: Analytics and AI for Healthcare
• Subjects: Social sciences, law, business > Business > Individual industrial sectors, branches; Artificial Intelligence, MEDICAL / Clinical Medicine, biotechnology, Chemical Engineering, Probability & statistics, Information technology: general issues, Engineering: general, Manufacturing industries, Probability and statistics, Information technology: general topics, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / Machine Learning, Pharmaceutical industries, Hydraulic Engineering, Industrial chemistry and chemical engineering