Intelligent Biomedical Technologies and Applications for Healthcare 5.0

1st Edition, Volume 16 - October 17, 2024
Editors: Lalit Garg, Gayatri Mirajkar, Sanjay Misra, Vijay Kumar Chattu
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 2 0 3 8 - 8
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 2 0 3 9 - 5

Intelligent Biomedical Technologies and Applications for Healthcare 5.0, Volume Sixteen covers artificial health intelligence, biomedical image analysis, 5G, the Internet of Medica… Read more

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Intelligent Biomedical Technologies and Applications for Healthcare 5.0, Volume Sixteen covers artificial health intelligence, biomedical image analysis, 5G, the Internet of Medical Things, intelligent healthcare systems, and extended health intelligence (EHI). This volume contains four sections. The focus of the first section is health data analytics and applications. The second section covers research on information exchange and knowledge sharing. The third section is on the Internet of Things (IoT) and the Internet of Everything (IoE)-based solutions. The final section focuses on the implementation, assessment, adoption, and management of healthcare informatics solutions.

This new volume in the Advances in Ubiquitous Sensing Applications for Healthcare series focuses on innovative methods in the healthcare industry and will be useful for biomedical engineers, researchers, and students working in interdisciplinary fields of research. This volume bridges these newly developing technologies and the medical community in the rapidly developing healthcare world, introducing them to modern healthcare advances such as EHI and Smart Healthcare Systems.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Contributors
About the editors
Preface
Chapter 1. Profiling and validating Fast Healthcare Interoperability Resource for maternal and neonatal child health referrals at primary healthcare level through BlockMom
1.1 Introduction
1.1.1 The global state of healthcare interoperability
1.1.2 Interoperability in LMICs
1.1.3 Health Level Seven FHIR
1.1.4 REpresentational State Transfer
1.1.5 Terminologies
1.1.6 Classification and coding in ICD
1.2 Methodology
1.2.1 Stakeholder interviews and dataset identification
1.2.2 FHIR profiling, validation, and publishing
1.3 Results
1.3.1 Interview outputs
1.3.1.1 Referral FHIR resource (known as referral letter or discharge letter)
1.4 Discussion
1.5 Limitations
1.6 Conclusion
Chapter 2. A cost-effective way of implementing big data in developing countries—Case of Malawi
2.1 Introduction and background
2.2 Literature review
2.2.1 Big data challenges in the health industry
2.2.2 Big data implementation
2.2.3 Big Data in Malawi Health Research Institutions
2.3 Analysis and design
2.3.1 Data analysis
2.3.1.1 Results of the survey
2.3.1.2 Big Data test environment
2.4 Implementation
2.4.1 Overview of the questionnaire design
2.4.2 Responses
2.4.3 Big Data test environment
2.4.3.1 Validating survey results
2.5 Results and evaluation
2.5.1 Testing the hypothesis
2.5.1.1 Big Data implementation in Malawi health research requires cost-effective solutions
2.5.1.2 Big Data in health research will improve decision-making and service delivery standards in the health sector
2.5.1.3 IT infrastructure is key to unlocking Big Data implementation in Malawi health research
2.5.2 Proposed guidelines to implement Big Data analytics
2.5.2.1 Business strategy
2.5.2.2 Understanding Big Data platforms
2.5.2.3 Data security
2.5.2.4 Cost
2.6 Conclusions and future scope
2.6.1 Academic application and limitations
2.6.2 Business application and limitations
2.6.3 Recommendations
2.6.4 Future scope
Appendix. Survey questionnaire
Chapter 3. Characterizing hospital admission patterns and length of stay in the emergency department at Mater Dei Hospital Malta
3.1 Introduction
3.1.1 Background and previous research
3.2 Materials and methods
3.2.1 Coxian phase-type distribution
3.2.2 Phase-type survival tree
3.3 Implementation
3.3.1 The dataset and some data analysis
3.3.2 Admissions data
3.3.3 Covariants
3.3.4 Phase-type survival trees
3.3.5 Length of stay analysis
3.3.6 Admissions analysis
3.3.7 Results and evaluations
3.3.8 Predictions
3.3.9 Personal characteristics model
3.3.10 Admissions analysis
3.3.11 Personal characteristics model
3.4 Conclusion
Chapter 4. Machine learning approach for enhancement in healthcare
4.1 Introduction
4.2 Popular machine learning algorithms in healthcare
4.3 How is machine learning used in healthcare?
4.4 Benefits of machine learning in healthcare
4.4.1 Faster data collection
4.4.2 Accelerated drug discovery and development
4.4.3 Cost-efficient processes
4.4.4 Personalized treatment
4.5 Top applications of machine learning in healthcare
4.5.1 Personalized treatment
4.5.1.1 Fraud detection and prevention
4.5.1.2 Detecting diseases in early stages
4.5.1.3 Robot-assisted surgery
4.5.1.4 Analyzing errors in prescriptions
4.5.1.5 Assisting in clinical research and trials
4.5.1.6 Drug discovery and development
4.5.1.7 Automating image diagnosis
4.6 Which machine learning algorithms are used the most in healthcare?
4.6.1 Artificial neural network
4.6.2 Logistic regression
4.6.3 Support vector machines
4.6.4 Convolutional neural network
4.6.5 Recurrent neural network
4.6.6 Discriminant analysis
4.6.7 Random forest
4.6.8 Linear regression
4.6.9 Naïve Bayes
4.6.10 K-nearest neighbor
4.7 Machine learning challenges in healthcare
4.7.1 Lack of data
4.7.2 Bias
4.7.3 Lack of strategy
4.7.4 Lack of in-house expertise
4.8 Conclusion
Chapter 5. Disease detection and treatment methods
5.1 Introduction
5.2 Literature survey
5.2.1 Prevention and management
5.2.2 Treatment
5.2.3 Conclusion
5.3 Proposed methodology
5.3.1 Research approach
5.3.2 Data collection
5.3.2.1 Primary data
5.3.2.2 Secondary data
5.3.3 Categorization and analysis
5.3.3.1 Categorization of disease types
5.3.3.2 Comparative analysis
5.3.4 Technological integration and ethical considerations
5.3.4.1 Role of technology and innovation
5.3.4.2 Ethical considerations
5.3.5 Future directions and implications
5.3.6 Validity and reliability
5.4 Comparative analysis and discussion
5.4.1 Traditional versus modern diagnostic techniques
5.4.2 Surgical versus minimally invasive interventions
5.4.3 Pharmacological versus targeted therapies
5.4.4 Conventional versus integrative medicine
5.4.5 Approaches
5.4.5.1 Approach for comparative analysis: Traditional versus modern diagnostic techniques
5.4.5.2 Approach for comparative analysis: Surgical versus minimally invasive interventions
5.4.5.3 Approach for comparative analysis: Pharmacological versus targeted therapies
5.4.5.4 Approach for comparative analysis: Conventional versus integrative medicine
5.5 Conclusion and future work
Chapter 6. Hybrid fuzzy-based improved particle swarm optimization technique for cancer cell detection
6.1 Introduction
6.2 Related works
6.3 Proposed system
6.3.1 Dataset
6.3.2 Preprocessing
6.4 Results and discussion
6.5 Conclusion
Chapter 7. A review on COVID-19 (SARS-CoV-2) pandemic: Using artificial intelligence and machine learning applications
7.1 Introduction
7.2 ML and AI recently employed to tackle health care SARS-CoV-2 outbreak
7.2.1 ML and AI technologies in SARS-CoV-2 screening and treatment
7.2.2 ML and AI technologies in SARS-Cov-2 contact tracing
7.2.3 ML and AI technologies in SARS-CoV-2 prediction and forecasting
7.2.4 ML and AI technologies in SARS-CoV-2 drugs and vaccination
7.3 Material
7.3.1 Statistical features of dataset used
7.3.2 Visual features of dataset
7.4 Method
7.4.1 The feature extraction techniques
7.4.1.1 Gray-level cooccurrence matrix
7.4.1.2 Local directional pattern
7.4.1.3 Gray-level run length matrix
7.4.1.4 Gray-level size zone matrix
7.4.1.5 Discrete wavelet transform
7.4.2 Support vector machines
7.5 Expected experimental results
7.6 Materials and methods
7.7 Expected results
7.7.1 COVID-19/normal classification studies
7.7.2 COVID-19/non-COVID-19 classification studies
7.8 Conclusion and discussion
Chapter 8. Validity, reliability, and usability assessment of smartphone-based Health 5.0 application for measuring chronic low back pain
8.1 Introduction
8.1.1 Significance of the study
8.1.2 Organization
8.2 Related work
8.2.1 Chronic pain: More than just a physical problem
8.2.2 Chronic low back pain: Definition and epidemiology
8.2.3 Mode of assessment
8.2.3.1 Limitations behind a paper-based assessment
8.2.3.2 The numerical rating scale
8.2.4 Technological advancement and its potential in healthcare
8.2.4.1 Lack of evidence in applications
8.2.5 Presenting a hypothesis
8.3 Problem formulation
8.4 Methodology
8.4.1 The user acceptance testing study design
8.4.2 Participants' inclusion and exclusion criteria
8.4.3 Unit study design
8.4.4 Tools
8.4.4.1 Testing procedure
8.4.4.2 Variables
8.4.4.3 Analysis of data
8.5 Results and discussions
8.5.1 Participants
8.5.2 Readings
8.5.3 Analysis of data
8.5.3.1 Normality
8.5.3.2 Correlation
8.5.3.3 Validity
8.5.3.4 Reliability
8.5.3.5 Usability
8.5.4 Other observations
8.5.4.1 Use of descriptions
8.5.4.2 Usability section of the questionnaire
8.5.4.3 Design section of the questionnaire
8.5.4.4 Quality of clinical content section of the questionnaire
8.5.5 Discussions
8.5.5.1 Validity
8.5.5.2 Reliability
8.5.5.3 Usability
8.5.5.4 Descriptions
8.5.5.5 Design section of the questionnaire
8.5.5.6 Quality of clinical content section of questionnaire
8.5.5.7 Hypothesis
8.5.5.8 Limitations
8.6 Conclusions and future scope
Chapter 9. Healthcare 5.0 opportunities and challenges: A literature review
9.1 Introduction
9.2 Sensors
9.2.1 Literature on sensors for Healthcare 5.0
9.3 Internet of Things
9.3.1 Literature on IoT for Healthcare 5.0
9.4 Telecommunication
9.4.1 Literature on telecommunication for Healthcare 5.0
9.5 Blockchain technology
9.5.1 Literature on blockchain for Healthcare 5.0
9.6 Artificial intelligence for Healthcare 5.0
9.6.1 Deep learning
9.6.1.1 Deep learning in EHRs
9.6.2 Federated learning
9.6.2.1 Benefits of federated learning
9.6.3 Literature on artificial intelligence for Healthcare 5.0
9.7 Cloud computing for Healthcare 5.0
9.7.1 Literature on cloud computing for Healthcare 5.0
9.8 Diagnosis
9.8.1 Literature on diagnosis for Healthcare 5.0
9.9 Observations, remarks, and noted points
9.9.1 Sensor
9.9.1.1 Advantages
9.9.1.2 Disadvantages
9.9.2 Internet of Things
9.9.2.1 Advantages of IoT
9.9.2.2 Disadvantages of IoT
9.9.3 Telecommunication
9.9.3.1 Advantages
9.9.3.2 Disadvantages
9.9.4 Blockchain technology
9.9.4.1 Advantages
9.9.4.2 Disadvantages
9.9.5 Artificial intelligence
9.9.5.1 Advantages
9.9.5.2 Disadvantages
9.9.6 Cloud computing
9.9.6.1 Advantages
9.9.6.2 Disadvantages
9.9.7 Diagnoses
9.9.7.1 Advantages
9.9.7.2 Disadvantages
9.10 Conclusion
Chapter 10. An impact of reliability in healthcare Internet of Things (HIoT)
10.1 Introduction
10.2 Trends of IoT in healthcare industry
10.3 Reliability: Scientific practices on IoT system
10.4 Reliability in healthcare Internet of Things
10.5 Conclusions
Chapter 11. Investigating the effect of a software intervention based on a theoretical behavior framework to encourage ergonomic compliance during computing device usage
11.1 Introduction
11.2 Background and review of literature
11.2.1 Negative health impacts of poor computing practices and prevention strategies
11.2.2 Behavior change theories and models
11.2.2.1 Health belief model
11.2.2.2 Theory of planned behavior
11.2.2.3 Information motivation behavioral model
11.2.2.4 Fogg behavior model
11.2.2.5 Persuasive systems design model
11.2.2.6 The behavior change wheel
11.3 Analysis and design
11.3.1 Framework survey instrument
11.3.1.1 Section 1: General
11.3.1.2 Section 2: Computing Device Usage
11.3.1.3 Section 3: Ergonomic Computing Device Practices
11.3.1.4 Section 4: Ergonomics Support Software
11.3.2 Framework survey data analysis
11.3.2.1 Demographic analysis
11.3.2.2 Self-reported ergonomic behavior of respondents
11.3.2.3 Desired elements of the ergonomics software
11.3.2.4 Willingness to use the software
11.3.2.5 Key elements of ergonomics software
11.3.3 Creation of the theoretical behavior framework
11.3.3.1 The EMOKAT theoretical behavior framework for computing ergonomics
11.4 Implementation
11.4.1 Soft ergonomics application prototype
11.4.1.1 Prototype design
11.4.2 Implementation tools
11.4.2.1 SQLite
11.4.2.2 C#.NET
11.4.2.3 Entity Framework
11.4.2.4 LINQ
11.4.3 Screen captures of soft ergonomics application prototype
11.5 Results and evaluation
11.5.1 Prototype evaluation plan
11.5.1.1 Prototype evaluation questionnaire
11.5.2 Findings
11.5.2.1 Usability
11.5.2.2 User satisfaction
11.5.2.3 Effectiveness of software
11.5.2.4 User self-efficacy
11.5.3 Effectiveness of the framework
11.5.3.1 Knowledge
11.5.3.2 Motivation, opportunity, and ability
11.5.3.3 Triggers
11.6 Conclusions and future scope
11.6.1 Lessons learned
11.6.2 Academic application and limitations
11.6.3 Business application and limitations
11.6.4 Recommendations/prospects for future research/work
Appendices: Framework questionnaire survey instrument
Part 1: General
Part 2: Computing Device Usage
Part 3: Ergonomic Computing Device Practices
Part 4: Ergonomics Support Software
Chapter 12. Machine learning approach for post-covid disease prediction
12.1 Introduction
12.2 Literature review
12.3 Data preparation
12.4 Data visualization
12.5 Materials and methods
12.6 Results and discussions
12.7 Conclusion
Chapter 13. Improving interoperability between health information technology systems used by mental health and acute hospitals
13.1 Introduction
13.2 Literature review
13.3 Problem statement
13.4 Analysis and design
13.4.1 Design methods (overview)
13.5 Results and evaluation
13.6 Conclusions and future scope
13.6.1 Academic application and limitations
13.6.2 Business application and limitations
13.6.3 Recommendations/prospects for future research/work
Chapter 14. Data management in the healthcare sector—A review
14.1 Introduction
14.2 Previous research outcomes and research gap (Table 14.1)
14.3 Bibliometric protocol
14.4 Cooccurrence mapping
14.4.1 Healthcare data sources
14.4.2 Cloud computing in healthcare data management
14.4.3 Healthcare data management tools
14.4.4 Blockchain technology in healthcare
14.5 Scope and approach
14.6 Conclusion
14.7 Areas for further study
Chapter 15. Navigating the frontier: Integrating emerging biomedical technologies into modern healthcare
15.1 Introduction
15.2 Section 1
15.2.1 Cutting-edge technologies in healthcare
15.3 Section 2
15.3.1 Applications of emerging technologies
15.4 Section 3
15.5 Factors related to commercial viability of intelligent biomedical technologies
15.5.1 Commercializable biointelligent technologies
15.6 Section 4
15.6.1 Challenges of intelligent biomedical technologies
15.6.2 Delimitations [76–79]
15.6.3 Proposed solutions to the challenges and delimitations
15.7 Section 5
15.7.1 Summary
15.8 Intelligent biomedical technologies versus sustainable development goals
15.9 Conclusion
Chapter 16. Healthcare cyber risk and its impact on healthcare
16.1 Introduction
16.2 Conceptual background and methodology
16.3 Literature review
16.3.1 Impact of cybersecurity risk on healthcare
16.3.2 Measures to improve safety in healthcare
16.3.3 Role of IoT devices in medical industry
16.3.4 Privacy issues in healthcare
16.3.5 Artificial intelligence in healthcare
16.3.6 Digitization of health industry
16.4 Conclusion
Chapter 17. Machine and deep learning techniques for smart healthcare industry: Big picture and open research challenges
17.1 Introduction
17.2 Research methodology
17.2.1 Research methods
17.2.2 Parametric constraint in “Biblioshiny” and quality assessment
17.3 Voice of academicians researchers
17.3.1 Main information about bibliometric review
17.3.2 Publications of smart healthcare
17.3.3 Citation analysis
17.3.3.1 Most cited articles
17.3.3.2 Prominent country and citation in smart healthcare field
17.3.4 Prominent journals
17.3.5 Prominent authors
17.3.6 Prominent institute
17.3.7 Keyword analysis
17.3.8 Net-Map analysis
17.3.9 Thematic and trend analysis
17.4 AI-driven healthcare: The big picture
17.4.1 Data collection
17.4.2 Data integration and storage
17.4.3 Data preprocessing
17.4.4 Machine learning
17.4.5 Clinical decision support
17.4.6 Patient engagement
17.5 Critical analysis of different diseases
17.5.1 Open research challenges in smart healthcare
17.6 Conclusions and future directions
Chapter 18. Precise brain stroke prediction using different machine learning algorithms
18.1 Introduction
18.2 Literature review
18.3 Methodology
18.3.1 Data acquisition
18.3.2 Preprocessing
18.3.3 Feature extraction
18.3.4 Machine learning model selection
18.3.5 Model training and validation
18.3.6 Results interpretation
18.4 Observations
18.4.1 Logistic regression
18.4.2 k-nearest neighbors
18.4.3 Decision tree
18.4.4 Support vector machine
18.5 Result
18.6 Conclusion
Index

Lalit Garg

Lalit Garg is a Senior Lecturer in Computer Information Systems at the University of Malta, Malta, and an honorary lecturer at the University of Liverpool, UK. He has been a researcher at the Nanyang Technological University, Singapore, and Ulster University, UK. He has supervised 200+ Master's dissertations, 2 DBA and 4 PhD theses and published 125+ high-impact publications in refereed journals/conferences/books, eight edited books and 22 patents. He has delivered several keynote speeches, organized/chaired international conferences, and consulted numerous public and private organizations for information systems implementation and management. His research interests are business intelligence, machine learning, data science, deep learning, cloud computing, mobile computing, the Internet of Things (IoT), information systems, management science and their applications, mainly in healthcare and medical domains. He participates in many EU and locally funded projects, including a one million euro Erasmus+ Capacity-Building project in Higher Education (CBHE), titled Training for Medical education via innovative eTechnology (MediTec), and Malta Council of science and technology's Space Research Funds. The University of Malta has awarded him the 2021-22 Research Excellence Award for exploring Novel Intelligent Computing Methods for healthcare requirements forecasting, allocation and management (NICE-Healthcare).

Affiliations and expertise

Senior Lecturer, Department of Computer Information Systems, Faculty of Information and Communication Technology, Europe, Malta

Gayatri Mirajkar

Gayatri Mirajkar (Senior Member, IEEE) received the Bachelor of Engineering degree in Electronics Engineering from Karmaveer Bhaurao Patil College of Engineering, Satara, India, the Master of Technology in Electronics and Telecommunication Engineering from Dr Babasaheb Ambedkar Technological University, Lonere, India, in 2005, and the PhD degree in Electronics Engineering from Shivaji University, Kolhapur, India in 2014. She has published several papers in journals and conferences. She has presented her research contributions at IEEE International Conferences in Greece and Hong Kong. She is currently working as Professor in the Electronics and Telecommunication Engineering department of Arvind Gavali College of Engineering, Satara, India. Her research interests include biomedical image analysis, signal denoising, biometrics, cognition, and restoration techniques.

Affiliations and expertise

Professor, Electronics and Telecommunication Engineering Department, Maharashtra, India

Sanjay Misra

Sanjay Misra, a senior member of IEEE and ACM Distinguished Lecturer, is a Senior Scientist at the Institute of Energy Technology (IFE), Halden, Norway. Before joining IFE, he was associated with the Computer Science and Communication department of Østfold University College, Halden, Norway. He has 25 years of wide academic administration and research experience in various universities in Asia, Europe, and Africa. He has authored more than 300 articles and received several awards for outstanding publications. He has delivered more than 100 keynote/invited speeches at several international conferences and institutes in more than 60 countries. His expertise is in the area of Applied Informatics (Cyber Security, Health Informatics, Software Engineering Applications, and Intelligent systems using AI and computational techniques). He has been amongst the top 2% of scientists in the world (published by Stanford University) for the last three consecutive years, ranked no. 2 in the whole of Africa in computer science (as per Elsevier: Scival analysis during 2017-2022) and also got several awards for outstanding publications (2014 IET Software Premium Award (UK), TUBITAK-Turkish Higher Education, and Atilim University). He is an Editor-in-Chief of IT Personnel and Project Management and International Journal of Human Capital and Information Technology Professionals (IGI Global), and editor in various journals and books.

Affiliations and expertise

Senior Scientist, Institute for Energy Technology, Department of Applied Data Science, Halden, Norway

Vijay Kumar Chattu

Dr Vijay Kumar Chattu is a Global Health Specialist at the University of Toronto and an Adjunct Professor at the School of Computer Science Engineering (SCOPE), Vellore Institute of Technology. Besides, he is an Adjunct Professor at the University of Alberta, a Senior Fellow at the WHO CC for KT and HTA in Health Equity at Ottawa and a Visiting Research Fellow at United Nations University-CRIS, Belgium. His interdisciplinary research intersects Medicine, Health Sciences, Health Technology, Health Policy and International Relations. During his academic career, Dr Chattu studied at various universities in 5 continents and travelled to over 45 countries. He did his MBBS and MD from India, MPH from Belgium, MPhil from South Africa, Fellowship in Digital Health/Occupational Medicine from Canada and PhD from Trinidad and Tobago. Besides, he also holds a Post Master’s Certificate in Mental Health from Harvard University and a Graduate Certificate in Global Health Diplomacy from Graduate Institute Geneva. He is a recipient of the NIH-Fogarty AITRP Grant at UCLA, the Fogarty MHIRT Grant at the U of Michigan and numerous prestigious awards from DGDC Belgium, Salzburg Global Seminar Austria and WHO-IARC France, to name a few. He published over 400 articles and has consistently ranked among the World’s Top 2% Scientists in Public Health by the Stanford University & Elsevier Rankings since 2021. Dr Chattu serves as an Associate Editor for BMJ Global Health and other prestigious journals. As a Global Health Policy advisor, he regularly contributes to the T20 Policy briefs for G20 Summits and G7 Forums. He has a long track record of consulting for multilateral organizations such as UNAIDS-TSF, WHO and the World Bank. Some of his co-authored works include applications of Blockchain technology, Artificial Intelligence, Machine Learning, and Deep Learning in health domains such as Disease Surveillance, Maternal and Child Health, Health Equity, Sleep Medicine etc. His current research interests include AI-driven diagnostics, Telemedicine, Remote patient consultations in the Metaverse, and Virtual Reality applications in medical education and training. Dr Chattu is also the Founder and CEO of Global Health Research and Innovations Canada Inc. (GHRIC) based in Toronto.

Affiliations and expertise

Senior Research Scientist, ReSTORE Lab, Department of Occupational Science & Occupational Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada

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Intelligent Biomedical Technologies and Applications for Healthcare 5.0

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Lalit Garg

Gayatri Mirajkar

Sanjay Misra

Vijay Kumar Chattu