Digital Transformation in Healthcare 5.0 : : Volume 2: Metaverse, Nanorobots and Machine Learning / / ed. by Rishabha Malviya, Sonali Sundram, Rajesh Kumar Dhanaraj, Seifedine Kadry.

• MitwirkendeR: Alam, Mehtab,; Angadi, Anupama,; Ashok, M.,; Fatima, Iram,; Georrge, John J.,; Gupta, Babita,; Jayashree, J.,; Jeba, Praba J.,; Kamineni, Shasank,; Karunakaran, Manivannan,; Kaur, Upinder,; Kaushik, Eva,; Kaushik, Rohit,; Kavitha, G.,; Keerthi Gorripati, Satya,; Kiran Kumar, M.V.,; Koduru, Tejodbhav,; Koteswara Rao, G.S.N.,; Krishnan, Batri,; Kumar Mishra, Saurav,; Kumar Singh, Deepak,; Kumar Yogi, Manas,; Kumar, Deepak,; Kumar, R.,; Mahesh, T.R.,; Malviya, Rishabha,; Manikandan, V. M.,; Mishra, Pragya,; Mohanasundaram, T.,; Nagaraj, S.,; Pandiarajan, T.,; Pedada, Saraswathi,; Radha, G.V.,; Raj Kumar, J.,; Rajnish, Ranjana,; Rani Budha, Roja,; Rao Patruni, Muralidhara,; Raza Khan, Ihtiram,; Reddy Mandhadi, Jithendar,; Sai Priya, Ch.E.N.,; Saravanan, R.,; Singh, Jaya,; Sundram, Sonali,; Tummala, Meghana,; Vashisht, Chirag,; Venkatachalam, Chandrasekar,; Vetrivel, S.C.,; Vijayashree, J.,; Yasheswini Kandimalla, Sai,
• HerausgeberIn: Kadry, Seifedine,; Kumar Dhanaraj, Rajesh,; Malviya, Rishabha,; Sundram, Sonali,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter, , [2024]; ©2024
• Year of Publication: 2024
• Language: English
• Series: Smart Computing Applications , 9/2
• Physical Description: 1 online resource (XIX, 378 p.)
• Other title: Frontmatter --; About the book --; Preface --; Foreword --; Contents --; List of contributors --; Chapter 1 The impact of blockchain technology on the healthcare system --; Chapter 2 The role of metaverse in transforming healthcare: blockchain approach --; Chapter 3 Blockchain-empowered metaverse healthcare systems and applications --; Chapter 4 Role of artificial intelligence in disease diagnosis --; Chapter 5 Machine learning for twinning the human body --; Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges --; Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare --; Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment --; Chapter 9 Nanorobots in healthcare --; Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy --; Chapter 11 Integration of cognitive computing and AI for smart healthcare --; Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope --; Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector --; Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning --; Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9783111398549
• ISSN: 2700-6239
• DOI: 10.1515/9783111398549
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: ed. by Rishabha Malviya, Sonali Sundram, Rajesh Kumar Dhanaraj, Seifedine Kadry.

The book "Digital Transformation in Healthcare 5.0: Metaverse, Nanorobots, and Machine Learning" is a comprehensive discussion of disruptive technologies and their applications in healthcare. The book starts with an overview of blockchain technology's impact on the healthcare sector, emphasizing its potential to improve data security and interoperability. The book also discusses the Metaverse's role in healthcare transformation, utilizing a blockchain method to improve patient care and medical practices. The book also focuses on the interrelationships of Blockchain-Enabled Metaverse Healthcare Systems and Applications, highlighting innovative strategies. It also introduces an Intraocular Pressure Monitoring System for Glaucoma Patients, demonstrating the integration of IoT and Machine Learning for improved care. The book winds up with a Machine Learning Approach to Voice Analysis in Parkinson's disease Diagnosis, demonstrating the potential of voice analysis as a non-invasive diagnostic tool.