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eBay-Artikelnr.:357596609213
Artikelmerkmale
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- Personalized
- No
- Country/Region of Manufacture
- United States
- ISBN
- 9781032780344
Über dieses Produkt
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
1032780347
ISBN-13
9781032780344
eBay Product ID (ePID)
20078208047
Product Key Features
Number of Pages
138 Pages
Language
English
Publication Name
Digital Twins : for Superior Clinical Decision Making
Subject
Clinical Medicine, Intelligence (Ai) & Semantics
Publication Year
2025
Type
Textbook
Subject Area
Computers, Medical
Series
Analytics and Ai for Healthcare Ser.
Format
Trade Paperback
Dimensions
Item Length
9.2 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Trade
Dewey Edition
23
Reviews
"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
Illustrated
Yes
Dewey Decimal
610.285
Synopsis
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, the 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 digital twins it is possible to reduce workflow challenges and provide fast and precise diagnosis. This then demonstrates how digital twins therefore support superior clinical decision-making. Importantly, the authors 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 IT practitioners. It also serves as a useful reference for senior-level undergraduate students and graduate students in health informatics and public health., 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., 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.
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- 2***3 (5485)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufThanks for a very smooth transaction!Irving Berlin's Annie Get Your Gun Musical Vocal Score (Nr. 357117203118)