| Description |
1 online resource (212 pages) |
| Series |
Healthcare technologies series ; 46 |
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Healthcare technologies series ; 46.
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| Contents |
Intro -- Title -- Copyright -- Contents -- About the editors -- 1 Introduction: digital twin technology in healthcare -- 1.1 Introduction -- 1.2 Digital twin -- background study -- 1.3 Research on digital twin technologies -- 1.4 Digital twin sectors in healthcare -- 1.4.1 Digital patient -- 1.4.2 Pharmaceutical industry -- 1.4.3 Hospital -- 1.4.4 Wearable technologies -- 1.5 Challenges and issues in implementation -- 1.5.1 Trust -- 1.5.2 Security and privacy -- 1.5.3 Standardization -- 1.5.4 Diversity and multisource -- References |
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2 Convergence of Digital Twin, AI, IOT, and machine learning techniques for medical diagnostics -- 2.1 Introduction -- 2.2 DT technology -- 2.2.1 Steps in DT creation -- 2.2.2 DT types and functions -- 2.3 DT and its supporting technologies -- AI, Cloud computing, DL, Big Data analytics, ML, and IoT -- 2.4 DT integration with other technologies for medical diagnosis and health management -- 2.5 DT technology and its application -- 2.5.1 DT application in manufacturing industry -- 2.5.2 Applications of DT in automotive & aerospace -- 2.5.3 Medicine diagnosis and device development |
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2.5.4 Wind twin technology -- 2.6 Conclusion -- References -- 3 Application of digital twin technology in model-based systems engineering -- 3.1 Evolution of DTT -- 3.2 Basic concepts of DTT -- 3.3 DTT implementation in power system -- 3.3.1 Characteristics of DTT in power systems -- 3.4 Power system network modeling using DTT -- 3.4.1 Model-based approach -- 3.4.2 Data-driven approach -- 3.4.3 Combination of both -- 3.5 Integration of power system with DTT -- 3.6 Future scope of DTT in power systems -- 3.7 Conclusion -- References |
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4 Digital twins in e-health: adoption of technology and challenges in the management of clinical systems -- 4.1 Introduction -- 4.2 Digital twin -- 4.3 Evolution of healthcare services -- 4.4 Elderly medical services and demands -- 4.5 Cloud computing -- 4.6 Cloud computing DT in healthcare -- 4.6.1 Use cases -- 4.7 Digital healthcare modeling process -- 4.8 Cloud-based healthcare facility platform -- 4.9 Applications of DT technology -- 4.9.1 Cardiovascular application -- 4.9.2 Cadaver high temperature -- 4.9.3 Diabetes meters -- 4.9.4 Stress monitoring -- 4.10 Benefits of DT technology |
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4.10.1 Remote monitoring -- 4.10.2 Group cooperation -- 4.10.3 Analytical maintenance -- 4.10.4 Transparency -- 4.10.5 Future prediction -- 4.10.6 Information -- 4.10.7 Big data analytics and processing -- 4.10.8 Cost effectiveness -- 4.11 DT challenges in healthcare -- 4.11.1 Cost effectiveness -- 4.11.2 Data collection -- 4.11.3 Data protection -- 4.11.4 Team collaboration -- 4.11.5 Monitoring -- 4.11.6 Software maintenance and assurance -- 4.11.7 Regulatory complications -- 4.11.8 Security and privacy-related issues -- 4.11.9 Targets of attackers -- 4.12 Conclusion -- References |
| Summary |
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. <italic>Digital Twin Technologies for Healthcare 4.0</italic> 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. 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 |
| Notes |
Description based on online resource; title from digital title page (viewed on July 19, 2023) |
| Subject |
Medicine -- Computer simulation
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Digital twins (Computer simulation)
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Medical innovations.
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Medical Informatics
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Digital twins (Computer simulation)
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Medical innovations
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| Form |
Electronic book
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| Author |
Dhanaraj, Rajesh Kumar, editor.
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Murugesan, Santhiya, editor
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Balusamy, Balamurugan, editor.
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Balas, Valentina Emilia, editor.
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Institution of Engineering and Technology, publisher.
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| ISBN |
1839535806 |
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9781839535802 |
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