Artificial Intelligence and Big Data Analytics for Smart Healthcare (Next Generation Technology Driven Personalized Medicine And Smart Healthcare)

✍ Scribed by Miltiadis D. Lytras (editor), Akila Sarirete (editor), Anna Visvizi (editor), Kwok Tai Chui (editor)

• Publisher: Academic Press
• Year: 2021
• Tongue: English
• Leaves: 277
• Edition: 1
• Category: Library

✦ Synopsis

Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate.

The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry.

✦ Table of Contents

Artificial Intelligence and Big Data Analytics for Smart Healthcare
Copyright
Dedication
Contents
List of contributors
Preface: artificial intelligence and big data analytics for smart healthcare: a digital transformation of healthcare primer
Introduction
Overview
List of Abstracts
Chapter 1 Healthcare in the times of artificial intelligence: setting a value-based context
Chapter 2 High-level strategy for implementing artificial intelligence (AI) at the Saudi Commission for Health Specialties ...
Chapter 3 Big data infrastructure: data mining, text mining, and citation context analysis in scientific literature
Chapter 4 Place attachment theories: a spatial approach to smart health and healing
Chapter 5 Utilizing IoT-based sensors and prediction model for health-care monitoring system
Chapter 6 QoS of mobile cloud computing applications in healthcare
Chapter 7 Analysis of Parkinson’s disease based on mobile application
Chapter 8 Mobile Partogram—m-Health technology in the promotion of parturient’s health in the delivery room
Chapter 9 Self-evaluation mobile application on mild cognitive impairment based on Mini-Mental State Examination with bilin...
Chapter 10 Spatiotemporal Big Data-driven vessel traffic risk estimation for promoting maritime healthcare: lessons learnt ...
Chapter 11 Neurofeedback using video games for attention-deficit/hyperactivity disorder
Chapter 12 Medical diagnosis in Alzheimer's disease based on supervised and semisupervised learning
Chapter 13 A support vector machine–based voice disorders detection using human voice signal
Chapter 14 COVID-19 detection from X-ray images using artificial intelligence
Chapter 15 Empowering the One Health approach and health resilience with digital technologies across OECD countries: the ca...
Chapter 16 An overview of artificial intelligence and Big Data Analytics for smart healthcare: requirements, applications, ...
Conclusion
References
Acknowledgments
1 Healthcare in the times of artificial intelligence: setting a value-based context
1.1 Introduction—mapping the current challenges in the health domain
1.2 Value-based approach to healthcare
1.3 Current state of artificial intelligence utilization in the health domain/artificial intelligence metaphors and its con...
1.4 Conclusion
References
Further reading
2 High-level strategy for implementing artificial intelligence at the Saudi Commission for Health Specialties
2.1 Introduction
2.2 Literature review
2.3 Current state of AI utilization at the SCFHS
2.3.1 Matching prospective trainees (residents) to residency training programs
2.3.2 Professional accreditation of health-care practitioners
2.3.3 ML for recommending (individualized) professional development activities and programs
2.3.4 The utility of natural language processing to improve performance at the SCFHS
2.3.5 The utility of robotics/RPA to improve performance at the SCFHS
2.3.5.1 Opportunities for implementing robotics/RPA at the SCFHS
2.3.5.2 Desired future state of robotics/RPA at the SCFHS
2.3.5.3 Important enablers and considerations for implementing robotics/RPA at the SCFHS
2.3.5.4 Potential impact of AI implementation on workforce and its dynamics at the SCFHS
2.4 AI implementation is an opportunity for successful human–machine collaboration
2.5 Conclusion and ethical considerations
References
Further reading
3 Big data infrastructure: data mining, text mining, and citation context analysis in scientific literature
3.1 Introduction
3.2 Literature review
3.3 Data and methodology
3.3.1 Data and preprocessing
3.3.2 Feature engineering
3.4 Results and discussion
3.4.1 Training and testing data
3.4.2 Discussion of ROC curves
3.4.3 Discussion on precision–recall curves
3.4.4 Discussion on important features
3.4.5 Evaluation
3.5 Concluding remarks
Appendix A
References
4 Place attachment theories: a spatial approach to smart health and healing
4.1 Introduction—smart healthcare, smart-home services, and the place attachment theory
4.1.1 Contributions
4.1.2 Linking this study to artificial intelligence and big data analytics
4.2 Literature review—using place attachment to define “home”
4.2.1 Home as a place for healing
4.2.2 Place attachment and the home environment
4.3 Methodology—case studies
4.3.1 Case study 1—smart lighting
4.3.1.1 Smart lighting creating a homely environment
4.3.2 Case study 2—IoT connectivity of devices
4.3.2.1 IoT creating homely environments
4.3.3 Case study 3—personalization of spaces
4.3.3.1 Smart-home technology in health-care environments
4.4 Implementation
4.4.1 A scenario of implementing the three case studies—St George’s Hospital, Port Elizabeth, and a three-dimensional analysis
4.5 Conclusion and recommendations
4.6 Future research
References
5 Utilizing IoT-based sensors and prediction model for health-care monitoring system
5.1 Introduction
5.2 Literature review
5.3 Health-care monitoring system
5.3.1 System design and implementation
5.3.2 Blood glucose prediction model
5.4 Result and discussion
5.4.1 Health-care monitoring system
5.4.2 Blood glucose prediction model
5.5 Conclusion
References
6 QoS of mobile cloud computing applications in healthcare
6.1 Introduction
6.2 Cloud computing and mobile cloud computing
6.3 QoS in CC and MCC
6.4 CC and MCC applications in the health area
6.5 New trends of security of CC in the health area
6.6 Evaluation of performance and QoS in the health area
6.7 Conclusion
References
7 Analysis of Parkinson’s disease based on mobile application
7.1 Introduction
7.2 Related work
7.3 Methods and materials
7.3.1 Monitoring and data collection
7.3.1.1 The manual dexterity
7.3.1.2 The walking test
7.3.1.3 The spatial memory test
7.3.1.4 A symptom questionnaire (life quality)
7.3.2 Data preprocessing
7.3.2.1 Integration of data sources and data filtering
7.3.2.2 Data transformation
7.3.2.3 Extraction of characteristics
7.4 Experimental results
7.4.1 The manual dexterity activity
7.4.2 The walking activity
7.4.3 The memory activity
7.5 Conclusion and future work
References
8 Mobile Partogram—m-Health technology in the promotion of parturient’s health in the delivery room
8.1 Introduction
8.2 The Mobile Partogram conception—m-Health technology in parturient care in the delivery room
8.3 Participatory user-centered interaction design to support and understand the conception of partograma mobile
8.4 Identifying needs and defining requirements
8.4.1 Design of alternatives
8.5 Building an interactive version (high-fidelity prototype)
8.6 Evaluation (usability)
8.7 Final considerations
8.8 Teaching assignments
References
9 Self-evaluation mobile application on mild cognitive impairment based on Mini–Mental State Examination with bilingual support
9.1 Introduction
9.1.1 Our contribution
9.2 Overview of the Mini–Mental State Examination
9.3 Our mobile application
9.3.1 Overview of the solution
9.3.2 User interface design for seniors and the elderly
9.3.3 Question types of the evaluation
9.3.4 Record tracking
9.4 Preliminary evaluation
9.4.1 Evaluation with users
9.4.2 Discussion with selected users
9.4.3 Feedbacks from nursing domain experts
9.5 Conclusion and future enhancement
References
10 Spatiotemporal Big Data-Driven Vessel Traffic Risk Estimation for Promoting Maritime Healthcare: Lessons Learnt from Ano...
10.1 Introduction
10.2 Ship domain
10.3 Proposed method
10.3.1 Trajectory data interpolation
10.3.2 Cross area calculation of ship domain
10.3.3 Ship collision risk assessment
10.4 Experimental results and analysis
10.4.1 The verification of Monte Carlo probabilistic algorithm
10.4.2 Simulate three situations of ship behavior
10.4.3 AIS data experiment
10.5 Conclusion
References
11 Neurofeedback using video games for attention-deficit/hyperactivity disorder
11.1 Introduction
11.2 Problems of ADHD
11.3 Background
11.3.1 Why neurofeedback
11.3.2 Limitations of neurofeedback
11.3.3 Treatments of ADHD
11.3.4 Supportive treatments
11.3.5 Neurofeedback training
11.3.6 Neurofeedback treatment protocols
11.3.6.1 Alpha protocol
11.3.6.2 Beta protocol
11.3.6.3 Alpha/theta protocol
11.3.6.4 Delta protocol
11.3.6.5 Gamma protocol
11.3.6.6 Theta protocol
11.3.6.7 Low-frequency versus high-frequency training
11.3.7 Hypothesis
11.3.8 Data collection
11.3.8.1 Visit to Hope Center
11.3.8.2 Observation results
11.3.8.3 Visit to King Faisal Specialist Hospital
11.3.8.3.1 Interview discussion
11.3.8.4 Online surveys
11.3.8.4.1 Survey results
11.3.8.4.2 Discussion
11.3.9 Game architecture
11.3.9.1 Game scenario
11.4 Conclusion and future recommendations
References
Further reading
12 Medical diagnosis in Alzheimer’s disease based on supervised and semisupervised learning
12.1 Introduction
12.2 Notations and review of related work
12.2.1 Notations
12.2.2 Linear discriminant analysis
12.2.3 Review of graph-based semisupervised learning
12.3 Trace ratio linear discriminant analysis for medical diagnosis: a case study of dementia via supervised learning
12.3.1 An improved algorithms for solving the trace ratio problem of TR-LDA
12.3.1.1 Convergence analysis of iterative trace ratio algorithm
12.3.1.2 Computation analysis
12.4 Identifying demented patients via TR-LDA
12.4.1 Data descriptions
12.4.2 Prediction stage
12.5 Simulations
12.5.1 Diagnosis results
12.5.2 Visualization
12.6 Compact graph-based semisupervised learning for medical diagnosis in Alzheimer’s disease: a case study of dementia via...
12.6.1 Review of graph construction
12.6.1.1 The compact graph construction
12.6.1.2 Symmetrization and normalization of graph weight
12.6.2 Identifying demented patients via compact graph semisupervised learning
12.6.2.1 Model stage
12.6.2.2 Diagnosis process
12.6.2.3 Out-of-sample inductive extension for new-coming data
12.6.3 Simulation
12.7 Conclusion
References
Further reading
13 A support vector machine–based voice disorders detection using human voice signal
13.1 Introduction
13.2 Literature review
13.3 Methodology of support vector machine–based voice disorders detection
13.3.1 Programming tool
13.3.2 VOIce ICar fEDerico II (VOICED) database
13.3.3 Feature extraction
13.3.4 Voice disorders detection using support vector machine
13.4 Performance evaluation of proposed support vector machine algorithm for voice disorders detection
13.5 Research challenges of smart health-care applications
13.5.1 Data collection
13.5.2 Data selection
13.5.3 Expenditure
13.5.4 New knowledge and skills to learn
13.5.5 Urban versus rural health
13.5.6 Linked databases
13.5.7 Optimizing treatment
13.5.8 Privacy
13.6 Research limitations and future research directions
13.7 Visions and conclusion
References
14 COVID-19 detection from X-ray images using artificial intelligence
14.1 Introduction
14.2 Deep learning in COVID-19 prognosis using X-ray images
14.3 Classification methods
14.3.1 Convolutional neural networks
14.3.2 Transfer learning
14.4 Results and discussion
14.4.1 Dataset
14.4.2 Experimental setup
14.4.3 Performance measures
14.4.4 Experimental results
14.4.5 Discussion
14.5 Conclusion
References
15 Empowering the One Health approach and health resilience with digital technologies across OECD countries: the case of CO...
15.1 Introduction
15.2 Aims and methodology of this study
15.3 Findings and suggestions regarding the research questions
15.3.1 The COVID–19 case in OECD countries: some background information
15.3.2 Digital technologies in the service of health and healthcare
15.3.3 Multidimensional framework and future recommendations
15.4 Conclusion
References
Further reading
16 An overview of artificial intelligence and big data analytics for smart healthcare: requirements, applications, and chal...
16.1 Introduction
16.2 Requirements of smart health-care applications
16.2.1 Mission critical applications
16.2.2 Scalable design
16.2.3 Cost-effective design
16.2.4 User-centered design
16.3 Smart health-care applications using AI and BDA techniques
16.3.1 Health-care monitoring and keeping well
16.3.2 Disease diagnosis and prediction
16.3.3 Drug discovery and development
16.3.4 Intensive care
16.3.5 Education and training
16.4 Challenges
16.4.1 Large-scale open health-care data
16.4.2 Technology transfer
16.4.3 Public acceptance in AI- and BDA-based applications
16.4.4 Policy establishment
16.5 Conclusion
References
Index