
Intelligent Biomedical Technologies and Applications for Healthcare 5.0
- 1st Edition, Volume 16 - October 17, 2024
- Imprint: Academic Press
- 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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Request a sales quoteIntelligent 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.
- Provides a comprehensive technological review of cutting-edge information in the wide domain of Healthcare 5.0
- Introduces concepts that combine computational methods, network standards, and healthcare systems to provide a much improved, more affordable experience delivered by healthcare services to its customers
- Presents innovative solutions utilizing informatics to deal with various healthcare technology issues
- 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
- Edition: 1
- Volume: 16
- Published: October 17, 2024
- Imprint: Academic Press
- No. of pages: 350
- Language: English
- Paperback ISBN: 9780443220388
- eBook ISBN: 9780443220395
LG
Lalit Garg
GM
Gayatri Mirajkar
SM
Sanjay Misra
VC
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.