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Blockchain and Machine Learning for e-Healthcare Systems

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Editors: Balamurugan Balusamy 1 ; Naveen Chilamkurti 2 ; Lucia Agnes Beena 3 ; T. Poongodi 1
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Publication Year: 2020

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.

Inspec keywords: distributed databases; medical computing; health care; cryptography; learning (artificial intelligence)

Other keywords: e-healthcare systems; data quality; patient history; drug verification; blockchain technology; personal information; consensus mechanisms; control access; medical data; ultra-secure immutable ledgers; reimbursement; machine learning; clinical trials; statistical data

Subjects: Data security; General and management topics; Distributed databases; Knowledge engineering techniques; Medical administration; Biology and medical computing

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