Blockchain and Machine Learning for e-Healthcare Systems
2: La Trobe University Australia, Melbourne, VIC, Australia
3: St. Joseph's College India, Bangalore, India
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
- Book DOI: 10.1049/PBHE029E
- Chapter DOI: 10.1049/PBHE029E
- ISBN: 9781839531149
- e-ISBN: 9781839531156
- Page count: 481
- Format: PDF
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Front Matter
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1 Blockchain technology and its relevance in healthcare
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Blockchain technology is extensively used in the recent years to maintain data security for the digital asset transformation without any third-party intervention. Blockchain is a rapidly evolving and emerging technology that has gained more attention from industry, academia and government sectors because of its latent relevance and offering potential solutions. The characteristics of blockchain made it as a cutting-edge technology and can be leveraged to solve significant issues in many promising applications. A plethora of blockchain application leverages and revolutionizes several disciplines with the potential of large-scale exploration. There is a promising future for the blockchain in the healthcare domain because of scalability, traceability, fault tolerance and high security. The benefits, challenges and relevant use cases for incorporating blockchain in the healthcare domain are presented by performing studies with in-depth analysis. The chapter provided a detailed study on the fundamentals of blockchain technology, types of blockchain technology and the relevance of blockchain in the healthcare industry along with several use cases. The research challenges and opportunities are also discussed in detail.
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2 Privacy issues in blockchain
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Blockchain technology is a new innovation of secure computing without centralized authority in an open -networked system as explained by Nakamoto. Blockchain stores the data in a distributed database that maintains a table of transaction record by arranging them into a hierarchical order of blocks. As far as security is concerned, blockchain is a decentralized peer -to -peer (P2P) network that is secured by an intelligent cryptography algorithm. Each node that is involved in the transaction is connected to the same blockchain data transaction itself since this has a distributed database. For example, in a digital currency, the blockchain is available to all such that entire transaction process can be monitored again to the first block. Bitcoin contains the false name in which the data nodes are not directly connected with the original data, but the repeated occurrences are associated with other. Blockchain is an innovation that is created by utilizing a mix of different strategies, for example, arithmetic, calculations, cryptography, and monetary models.
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3 Reforming the traditional business network
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Blockchain development has expanded amazing thought, with an elevating eagerness for a lot of different applications, running from data the administrators, cash-related organizations, advanced cybersecurity, Internet of Things (IoT), and sustenance science to social protection industry and psyche inspect. The board has identified a stunning interest in using blockchain work opportunities for the transfer of sealed and secure social protection data. Already, there is a blockchain that is improving the standard social insurance practices to a legitimately solid recommendation, like successful end and treatment through guaranteed and secure information sharing. Later on, blockchain could be improved, which may conceivably help in changed, authentic, and secure remedial administrations by consolidating the whole consistent clinical information of a patient's prosperity and introducing it in a top-tier secure medical organizations course of the plan. In this work, we study both the present and most recent updates in the field of medical organizations by acknowledging blockchain as a model. We, in addition, talk about the employments of blockchain, near to the difficulties opposed and future points of view. The biggest problem connected with blockchain is a lack of technical knowledge. Trust is built in the network and blockchain invention, and less in the individual participants or the sharing of accomplices. High structure and operational consistency thus maintain the namelessness and pseudonymity of the participants. It reduces the lopsided characteristics of power, and the man handles and the arbitrary decisions.
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4 A deep dive into Hyperledger
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Hyperledger is an open -source, network -oriented effort made to propel cross industry blockchain developments. It is a worldwide facilitated exertion remembering pioneers for banking, cash, Internet of Things, manufacturing, supply chains, and advancement. The Linux Foundation has Hyperledger under the establishment. This chapter gives an elevated level overview of Hyperledger: why it was made, how it is represented, and what it would like to accomplish. The core of this chapter presents five convincing uses for big business blockchain in various ventures. It depicts how the Hyperledger guarantees the secure, progressively solid, and increasingly streamlined communication.
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5 Machine learning
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Machine learning (ML) is a method of data analysis that automates analytical model building. Here the systems can learn from data, identify patterns and make decisions with minimal human interference. ML is an application of artificial intelligence (Al) that provides systems the ability to automatically learn and improve without being clearly programmed. It focuses on the development of computer programs that can access data and use them in learning. The process of learning begins with observations, like direct experience, or instructions, in order to fi nd patterns in data and make better decisions for the future.
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6 Machine learning in blockchain
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The promise and potential of blockchain to drive social impact is enormous. Blockchain will touch every significant industry which people interact with in dayto-day life. Blockchain will enable solutions that are not previously possible. Health sector recently attracted more initiatives than any other industry. Applications for blockchain in health include digital health records exchange and pharmaceutical supply chain management. In many of these areas, blockchain offers a more secure, decentralized and efficient solution than would otherwise be possible. Blockchain and machine learning (ML) technologies are gaining strong momentum and thrust around the world. Blockchain, a disruptive technology, made its big splash with crypto currencies invention and trading. On the other hand, with predictive and descriptive algorithms, ML is making considerable waves in harnessing existing data to identify patterns and gain insights. Congregating the two technologies can only make them super disruptive! Both have the potential to hasten data exploration and analysis as well as intensify transactions security. Additionally, distributed blockchains can be a significant and proven input for ML, which requires big datasets to make quality predictions. It goes without saying that each technology has its degree of complexity, but both artificial intelligence (AI) and blockchain are in situations where they can benefit from each other and help one another. Both these technologies are able to effect and enact upon data in different ways as their combination makes sense, which can take the exploitation of data to new levels. At the same time, the integration of ML and AI into the blockchain, and vice versa, can enhance blockchain's underlying architecture and boost AI's potential. Additionally, blockchain can also make AI more coherent and understandable for tracing and decision-making using ML techniques. Blockchain and its ledger can record all data and variables that go through a decision made under ML. The present chapter focuses on a few standard ML algorithms that are useful in supporting blockchain technology.
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7 Framework for approaching blockchain in healthcare using machine learning
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Artificial intelligence (AI) acts an integral portion of computer technology segment which underscores the pattern of intellectual machineries that exert, contemplate and react similar to humans. It is the concept and exercise of building machineries that are adept of accomplishing chores which appear to necessitate acumen. It comprises semantic networks, machine learning (ML) and deep learning perceptions. On the other hand, the blockchain permits digital data to be distributed, but not plagiarized, and it is an emerging technology which emphasizes on interoperability. It is an open structure with chunks of data associated conjointly that encompass citations to the leading block. It is a distributed and decentralized system like an uncluttered journal that accumulates an archive of possessions and dealings in a peer-to-peer system. Blockchain and fusion of ML into conventional merchandizes and its correlated amenities can produce ample prospects for establishments. This chapter explores the abilities predictable at the integration of ML and blockchain, in particular in the field of healthcare and to examine about the typical descriptions, advantages and trials of this coalition.
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8 Reforming the traditional business network
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Today the use of internet provides so much information and knowledge related to healthcare sector that everyone gets gradually forced to use it. This scenario not only saves our time but also saves a large amount of money that many would otherwise spend for physical visit into the hospitals and clinics. Today peoples have already got used to the large number of electronic healthcare methods. It is quite interesting about how fast the healthcare industry is growing with the use of advanced technologies. In the field of healthcare, organizations are utilizing emerging technologies, mainly machine learning (ML) and deep learning. Artificial intelligence (AI) and block chain could turn to establish something exponential in this area. It is clear that the ML and AI applications widely enhance the easiness, accuracy and speed of the diagnosis. The AI tools and algorithms help in analyzing the information more deeply and quickly with high accuracy; hence, the doctors are to be more precise with the diagnosis. Many areas such as image processing, X-ray analysis, bone age calculations and radiology are enhanced by the applications of AI. The blockchain technology is a real timestamped series of the immutable files and records of the data that would be managed by a group of computers that do not belong to any single entity. Storing and retrieving medical and healthcare sector data are more secure with the blockchain. The blockchain tools and applications help in ensuring the global integrity of the medical records. Each and every day the business industries, especially medical and healthcare sectors, are enhanced by the immense usage of adaptive technologies, and also they are establishing well-defined networks across all the interacting participants.
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9 Healthcare analytics
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Healthcare is becoming very complex day by day. The data produced by healthcare is so complex that someday it would become difficult to maintain the quality of the healthcare data. A large amount of data is produced by hospitals and other medical institutes, and it is becoming difficult to find what exactly is needed. The healthcare analysis is not only helpful for patients but also for the hospitals which take care of the patients pre- and post-hospitalization. Managing healthcare data also enhances the involvement of patients with the predictive modelling and analysis based on the healthcare data. There are many sources from where a lot of healthcare data can be collected such as electronic medical records (EMR), pathology labs, immunization programs and different surveys in medical camps. These sources give data in multiple formats; so analysing the healthcare data becomes much more complex and difficult. Many different organizations manage their healthcare data using different technologies. In this chapter, we would be discussing various emerging technologies for the healthcare analytics. We would also be discussing various software which help in analysing the healthcare data and the challenges associated with the healthcare analytics.
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10 Blockchain for healthcare
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In 10 years the electronic medical record will be the minor player, in terms of where a person's health history lives. Most of that information will be kept on the phone or in a secure cloud, and the patient will be highly engaged with collecting, curating and sharing that data. Most doctor visits will be like calling up a YouTube meets virtual human docs and there will also be an aspect of virtual reality.
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11 Improved interop blockchain applications for e-healthcare systems
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Blockchain in healthcare plays a vital role in the form of encryption-based technology, which has been used to keep the patients' data in a secured and more distributed way. Blockchain technology in E-healthcare is also being used for digital payment systems that focus on cybersecurity, EHRs (electronic health records), patients data management, and so on. The framework has been designed to describe the major obstacles to the adaptation of blockchain technology in E -healthcare, such as data security, interoperability, data integrity, identity validation, and scalability. In addition, here comes the Ethereum as new technology based on blockchain technology. Ethereum is used for developing distributed applications (DApps) and replacing the Internet of third parties which store data, transactions and keep track. Using Ethereum, there is a chance to recreate the Internet without the involvement of third parties. The process of elimination of the third party could be implemented using smart contracts by creating trust, and Ethereum uses consensus proof -of -work (PoW), and Ethereum provides a peer -to -peer network to eliminate the involvement of third parties. The proposed system is a proof -of -concept implementation of a blockchain for research in clinical trials.
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12 Blockchain: lifeline care for breast cancer patients in developing countries
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The motivation behind this section is to give an outline of the blockchain innovation in the social insurance framework. This chapter covers the mechanical structure for storing and retrieving the clinical records of patients in blockchain alongside gracefully chain of the executives of medications utilizing keen agreements. Be that as it may, blockchain is not a solution to malignant growth, yet it fills in as a potential innovative instrument to battle against bosom diseases.
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13 Machine learning for health care
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Machine learning (ML) is predominately being used to solve various technical and nontechnical challenges around the world. Taking the context of health care, ML took over the control and helping many practitioners for effective decision-making. Practically, this is once again proved by the researchers as they are using the ML algorithms for fast detection of COVID-19 and steps are initiated for the drug discovery of the same. In this chapter, we initially discuss the work happening in COVID-19 using ML algorithms. Then, we summarize the role of ML for analyzing and assessing various chronic diseases. Even though ML algorithms are predicting multidimensional aspects of the target disease, the experts in the field are still hesitating to use those outcomes as they lack in justification. To address this, a separate concept called explainable AI (XAI) is discussed. Data scientists are using ML algorithms to address chronic health issues with less cost and in a more accurate way. The question is, how ML could achieve this? This will be the main motivation for the entire chapter. In a recent report of a Harvard Medical Survey, it said that nearly 5,000,000 Indians die every year due to the medical errors (Harvard, 2020). These errors are getting reduced when the same disease is analyzed with ML. There is a famous proverb, “Prevention is better than cure.” According to World Health Organization, in most of the cases, chronic diseases cannot be prevented. But, the impact of them can be reduced with proper data collection and effective analysis using advanced ML algorithms. For instance, to detect lung cancer, it would cost hundreds of dollars for undergoing diagnosis and medication. Above that, if the disease is detected in the advanced stages then the probability of the cure is also very less. All these negative consequences can be reduced drastically when the usage of ML for health care is further exploited.
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14 Machine learning in healthcare diagnosis
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The emerging healthcare diagnosing system is the main core part of hospital management and widely used applications for diagnosing the human body, to fi nd the disease in a mining manner by applying new technologies in image processing with machine learning (ML) intelligent system. In health diagnosing systems, the automatic processes are unimaginable, and it is reducing the time and complexity of tasks. For the automatic process of diagnosis systems, a vast number of techniques and methodologies are introduced and applied to perform the intended diagnosis tasks. ML is an inevitable tool in the medical diagnosis system, and it is the most important application of artificial intelligence (Al).
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15 Python for healthcare analytics made simple
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Machine learning (ML) is found to be an element of healthcare industries for the past two decades after it was initially implemented for monitoring the antibiotic doses for patients who suffer from various infections. Nowadays, the volume of electronic healthcare records (EHR) gets increased, and thus, it leads to the huge massive of genetic sequential data which in turn directs the healthcare's importance in ML. A brief discussion about the common data sources in healthcare would be explained in detail. From various inferences, it is found that Python is a go-to language for developers, and it is extensively used in several fields. This chapter gives a brief description of the characteristics of data and the significance of the data quality in healthcare. Data has to be extracted from various sources for better analysis. Hence, the chapter puts forth an overview of challenges that must be resolved, and various types of extraction tools would be discussed. To describe the perceptions that are obtained through the analysis of the large data sets, data visualization is immensely used. In this world of healthcare, data analytics is huge, and it could include an extensive variety of organizations and other uses cases such as emergency rooms (ER), intensive care units, hospitals, and medical equipment manufacturers. The function of data visualization in the data science process flow could be discussed in detail in addition to the several techniques used to denote the complex data. It also covers advanced visualization techniques that emphasize grid, wordcloud, heatmap, and geospatial. Data analytics is a buzzword that refers to the diversified types of analysis. Perception is required since more information is required by the users for an extensive analysis. Certain techniques for the management of the data and analysis need more efforts and continuous attention particularly for data aggregation, data capture, real-time data streaming, analytics, and other visualization solutions, so that the integration could be done further for the improved utilization of electronic medical record (EMR) with the healthcare. Finally, this chapter briefs about the challenges that could be identified with the vast amount of data composed as EMR.
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16 Identification and classification of hepatitis C virus: an advance machine-learning-based approach
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Hepatitis C virus (HCV) is identified as one of the leading sources of liver disease transmitted through blood-to-blood contact worldwide. HCV contamination is flattering a foremost universal health challenge, and due to its complications, more than 3 million new infectious patients along with 350,000 deaths are occurring every year. In the future, hepatitis C (HC) may be considered as one of the reasons for malaise and fatality of human, as it has been estimated that nearly 170 million have been infected by this. The last decades of medical research are evident that detecting and finding solutions for HC has remained a major concern in Egypt. As Egyptian blood donors were found highest among other blood donors from all nationalities, HCV became a major community health concern. To cope with such a problem, some of the statistical-based approaches are being developed and became a partial solution to some extent. To address the challenges of healthcare, a wide range of tools, techniques, and frameworks have been offered by machine learning (ML). As ML approaches have the capability of determining and recognizing patterns in complex datasets, they are identified as the best connectionist systems to predict the future outcomes of the HCV. In this chapter, we propose experimental investigations on the study of various ML approaches for the diagnosis of associated risk factors, cofactors promoting its progression, complications in the prevention and control of HCV in Egypt. Further, the project will focus on some of the basic ML strategies along with the challenges of handling the HC disease.
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17 Data visualization using machine learning for efficient tracking of pandemic - COVID-19
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From the first case in December 2019 to more than 2.92 million cases in just 3 months, COVID-19 became a pandemic. COVID-19 is spreading all around the world, and due to this pandemic situation, humans' life is at risk. On one side, healthcare and sanitization workers are stretching themselves to deal with this situation at the frontline, and on the other, data scientists and machine learning (ML) experts are researching to provide data in an understandable form to the world. This chapter provides the details of different ways of processing and visualizing the huge amount data generated on this pandemic. This includes the clusters on the basis of symptoms in different age groups, effects of COVID-19 on different countries, etc.
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Back Matter
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