Main description:

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.

Contents:

Chapter 1: Blockchain technology and its relevance in healthcare
Chapter 2: Privacy issues in blockchain
Chapter 3: Reforming the traditional business network
Chapter 4: A deep dive into Hyperledger
Chapter 5: Machine learning
Chapter 6: Machine learning in blockchain
Chapter 7: Framework for approaching blockchain in healthcare using machine learning
Chapter 8: Reforming the traditional business network
Chapter 9: Healthcare analytics
Chapter 10: Blockchain for healthcare
Chapter 11: Improved interop blockchain applications for e-healthcare systems
Chapter 12: Blockchain: lifeline care for breast cancer patients in developing countries
Chapter 13: Machine learning for health care
Chapter 14: Machine learning in healthcare diagnosis
Chapter 15: Python for healthcare analytics made simple
Chapter 16: Identification and classification of hepatitis C virus: an advance machine-learning-based approach
Chapter 17: Data visualization using machine learning for efficient tracking of pandemic - COVID-19