Blockchain and machine learning technologies can mitigate healthcare issues such as slow access to medical data, poor system interoperability, lack of patient agency, and data quality and quantity for medical research. Blockchain technology facilitates and secures the storage of information in such a way that doctors can see a patient's entire medical history, but researchers see only statistical data instead of any personal information. Machine learning can make use of this data to notice patterns and give accurate predictions, providing more support for the patients and also in research related fields where there is a need for accurate data to predict credible results.
This book examines the application of blockchain technology and machine learning algorithms in various healthcare settings, covering the basics of the technologies and exploring how they can be used to improve clinical outcomes and improving the patient's experience. These topics are illustrated with reference to issues around the supply chain, drug verification, reimbursement, control access and clinical trials. Case studies are given for applications in the analysis of breast cancer, hepatitis C, and COVID-19.
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Dr. Balamurugan Balusamy has served up to the position of Associate Professor in his stint of 14 years of experience with VIT University, Vellore, Tamil Nadu, India. He has completed his Bachelors, Masters and Ph.D. Degrees from top premier institutions. His passion is teaching and adapts different design thinking principles while delivering his lectures. He has done around 30 books on various technologies and visited more than 15 countries for his technical discourse. He has several top notch conferences in his resume and has published over 150 quality journal, conference and book chapters combined. He serves in the advisory committee for several startup and forums and does consultancy work for industry on Industrial IoT. He has given over 175 talks in various events and symposium. He is currently working as a professor in Galgotias University and teaches students, does research on blockchain and IoT.
Dr. Naveen Chilamkurti is currently Acting Head of Department, Computer Science and Computer Engineering, La Trobe University, Melbourne, VIC, Australia. He obtained his Ph.D. degree from La Trobe University. He is also the Inaugural Editor-in-Chief for International Journal of Wireless Networks and Broadband Technologies launched in July 2011. He has published about 165 journal and conference papers. His current research areas include intelligent transport systems (ITS), wireless multimedia, wireless sensor networks, and so on. He currently serves on the editorial boards of several international journals. He is a Senior Member of IEEE. He is also an Associate editor for Wiley IJCS, SCN, Inderscience JETWI, and IJIPT.
Dr. T. Lucia Agnes Beena is working as an Assistant Professor in the department of Information Technology, St. Joseph's College, Tiruchirappalli, Tamil Nadu, India. She has 18 years of teaching experience and 6 years of research experience. She has published number of research articles in Scopus Indexed Journals. She authored one book and published few chapters with reputed publishers. Her areas of interest are cloud computing, big data and psychology of computer programming.
Dr. T. Poongodi is working as an Associate Professor in the School of Computing Science and Engineering, Galgotias University, Delhi - NCR, India. She has completed Ph.D. in Information Technology (Information and Communication Engineering) from Anna University, Tamil Nadu, India. She is a pioneer researcher in the areas of big data, wireless ad-hoc network, internet of things, network security and blockchain technology. She has published more than 50 papers in various international journals, national/international conferences, and book chapters in CRC Press, IGI Global, Springer, Elsevier, Wiley, De Gruyter and edited books in CRC, IET, Wiley, Springer.
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Hardback. Condición: New. Blockchain and machine learning technologies can mitigate healthcare issues such as slow access to medical data, poor system interoperability, lack of patient agency, and data quality and quantity for medical research. Blockchain technology facilitates and secures the storage of information in such a way that doctors can see a patient's entire medical history, but researchers see only statistical data instead of any personal information. Machine learning can make use of this data to notice patterns and give accurate predictions, providing more support for the patients and also in research related fields where there is a need for accurate data to predict credible results. This book examines the application of blockchain technology and machine learning algorithms in various healthcare settings, covering the basics of the technologies and exploring how they can be used to improve clinical outcomes and improving the patient's experience. These topics are illustrated with reference to issues around the supply chain, drug verification, reimbursement, control access and clinical trials. Case studies are given for applications in the analysis of breast cancer, hepatitis C, and COVID-19. Nº de ref. del artículo: LU-9781839531149
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Hardback. Condición: New. Blockchain and machine learning technologies can mitigate healthcare issues such as slow access to medical data, poor system interoperability, lack of patient agency, and data quality and quantity for medical research. Blockchain technology facilitates and secures the storage of information in such a way that doctors can see a patient's entire medical history, but researchers see only statistical data instead of any personal information. Machine learning can make use of this data to notice patterns and give accurate predictions, providing more support for the patients and also in research related fields where there is a need for accurate data to predict credible results. This book examines the application of blockchain technology and machine learning algorithms in various healthcare settings, covering the basics of the technologies and exploring how they can be used to improve clinical outcomes and improving the patient's experience. These topics are illustrated with reference to issues around the supply chain, drug verification, reimbursement, control access and clinical trials. Case studies are given for applications in the analysis of breast cancer, hepatitis C, and COVID-19. Nº de ref. del artículo: LU-9781839531149
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