In recent years, new applications on computer-aided technologies for telemedicine have emerged. Therefore, it is essential to capture this growing research area concerning the requirements of telemedicine. This book presents the latest findings on soft computing, artificial intelligence, Internet of Things and related computer-aided technologies for enhanced telemedicine and e-health. Furthermore, this volume includes comprehensive reviews describing procedures and techniques, which are crucial to support researchers in the field who want to replicate these methodologies in solving their related research problems. On the other hand, the included case studies present novel approaches using computer-aided methods for enhanced telemedicine and e-health. This volume aims to support future research activities in this domain. Consequently, the content has been selected to support not only academics or engineers but also to be used by healthcare professionals.
Author(s): Gonçalo Marques, Akash Kumar Bhoi, Isabel de la Torre Díez, Begonya Garcia-Zapirain
Series: Studies in Fuzziness and Soft Computing, 410
Publisher: Springer
Year: 2021
Language: English
Pages: 368
City: Cham
Preface
Contents
Intelligent Systems for Enhanced Telemedicine
Research Challenges and Opportunities Towards a Holistic View of Telemedicine Systems: A Systematic Review
1 Introduction
2 Study Design
3 A Holistic Taxonomy of Telemedicine System and Its Challenges
3.1 Artificial Intelligence in Telemedicine
3.2 Human Computer Interaction in Telemedicine
3.3 Security in Telemedicine
3.4 Towards Secure Telemedicine Systems
4 Discussion
4.1 Findings and Limitations
5 Conclusion
References
Telemedicine in the Current New Normal: Opportunities and Barriers
1 Introduction
2 Success Factors
3 Roles of IoT, AI, and Other Relevant Technologies
4 Opportunities
5 Barriers
6 Discussion
7 Conclusions
References
Teledentistry: A New Approach in Dental Medicine
1 Introduction
2 Types of Teleconsultation
3 COVID-19
4 Applications in the Different Fields of Dentistry
4.1 Oral Medicine
4.2 Maxillofacial Field
4.3 Orthodontics
4.4 Traumatology
4.5 Periodontics
4.6 Caries
4.7 Endodontics
4.8 Paediatrics
4.9 Prosthodontics and Gerodontology
4.10 Education
5 Dentist’s Perceptions
6 Patient Acceptance
7 Pros and Cons
7.1 Efficiency, Cost-Effectiveness
8 Ethical and Legal Aspects
8.1 Privacy, Anonymity and Security
8.2 Informed Consent
8.3 Patients’ Rights
9 E-prescibing
10 Discussion
11 Conclusions
References
Smart Management of Telemedicine Rooms in an e-Hospital Emergency Department
1 Introduction
2 Case Study Definition
3 Approach for the Telemedicine Rooms’ Smart Management
4 Controller Tuning Using Soft Computing Techniques
5 Results and Discussion
5.1 Genetic Algorithm
5.2 Memetic Algorithm
5.3 Overall Comparison
6 Conclusions
References
An IoT Cloud Model for Diabetes Home-Based Care: A Case Study for Perceived Future Feasibility
1 Introduction
2 Background
3 Methods
4 Results
4.1 Case Study Results: Step 1
4.2 Case Study Results: Step 2
5 Discussion
6 Conclusion
References
Internet of Things for Medical Decision Making
IoT for Enhanced Decision-Making in Medical Information Systems: A Systematic Review
1 Introduction
2 Methodology
3 Results and Discussion
4 Conclusion
References
From the Internet of Things to an Internet of Services in Healthcare
1 Introduction
2 The Internet of Things
2.1 Description of the Idea ‘IoT’. Technical Limits
2.2 Description of the Idea ‘IoT in Healthcare’: Medical Perspectives
3 From the Internet of Things to an Internet of Services
4 Application Domains and Next Steps
5 Current Research Activities
6 Conclusions
References
Machine Learning and Internet of Things for Smart Living: A Comprehensive Review and Analysis
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Automated Decentralized IoT Based Blockchain Using Ethereum Smart Contract for Healthcare
1 Introduction
2 Background
2.1 Blockchain
2.2 Ethereum Blockchain
2.3 Consensus Algorithms
3 IoT Based Blockchain Architecture for Healthcare
4 Related Work
5 Proposed Architecture for Healthcare Network
6 Results and Discussions
7 Conclusions
References
MIoT-Based Big Data Analytics Architecture, Opportunities and Challenges for Enhanced Telemedicine Systems
1 Introduction
2 Medical Internet of Things in Healthcare System
3 Big Data and Big Data Analytics on the Medical Internet of Things
3.1 Big Data Analytics Life Cycle
4 Big Data Analytics Opportunities in MIoT-Based Platform
5 The Challenges of Big Data Analytics in Medical Internet of Things
6 Framework for MIoT-Based Big Data Analytics
6.1 Use Case of the Proposed Framework
7 Discussion and Future Research Directions
8 Conclusion
References
A Comprehensive Review on the Challenges for Intelligent Systems Related with Internet of Things for Medical Decision
1 Introduction
2 Healthcare Internet of Things (HIoT)
2.1 Data Acquisition
2.2 Communication Layer
2.3 Processing Layer and Cloud Computing
3 Intelligent Systems for Decision Making
3.1 Time Sensitive Decisions
3.2 Diagnosis of Serious Diseases
3.3 Hospital Resources and Management
4 Health Recommendation Systems
5 Conclusion
References
Soft Computing and Artificial Intelligence for Enhanced Health
Artificial Intelligence and Machine Learning for Health Risks Prediction
1 Introduction
2 Background
2.1 Evolution of Modern Healthcare Systems
2.2 Health Risk Modelling and Prediction
2.3 Artificial Intelligence and Machine Learning
3 Global Overview of Major Illnesses and Diseases
3.1 Major 10 Diseases Globally
3.2 The Novel Coronavirus (COVID-19)
4 State-of-the-Art: Machine Learning Applications in Healthcare
4.1 Application Overview
4.2 Review of Machine Learning in Disease Prediction
5 Towards a Roadmap for Health Risks Modelling and Prediction
6 Conclusion
References
Multimodal Deep Learning for Computer-Aided Detection and Diagnosis of Cancer: Theory and Applications
1 Introduction
2 Background
3 Multimodal Cancer CAD Systems
3.1 Brain Cancer
3.2 Lung Cancer
3.3 Hepatocellular Carcinoma
3.4 Other Cancer Types
4 Discussion
5 Conclusion
References
Disease Prediction Using Artificial Intelligence: A Case Study on Epileptic Seizure Prediction
1 Introduction
2 Disease Prediction
3 Artificial Intelligence Techniques for Disease Prediction
3.1 Artificial Neural Networks
3.2 Deep Learning
3.3 Convolutional Neural Networks
4 Case Study of Epileptic Seizure Prediction
4.1 Biomedical Signal Denoising
4.2 Feature Extraction
4.3 Dimension Reduction
4.4 Prediction and Classification
4.5 Experimental Data
4.6 Performance Evaluation Measures
4.7 Experimental Results
5 Discussion
6 Conclusion
References
A Novel Wrapper-Based Feature Selection for Heart Failure Prediction Using an Adaptive Particle Swarm Grey Wolf Optimization
1 Introduction
2 Literature Review
2.1 Related Works
2.2 Machine Learning Algorithms
3 Materials and Methods
3.1 Data Collection
3.2 Data Preprocessing
3.3 Research Methodology
4 Results and Discussion
4.1 Performance Evaluation
4.2 Analysis Results Using Various Machine Learning Models
4.3 Experimental Results of APSGWO-MLP
5 Conclusion
References
Affective Computing and Emotion-Sensing Technology for Emotion Recognition in Mood Disorders
1 Introduction
2 Theories and Classification Models of Emotion
3 Automating Emotion Recognition and Evaluation Systems
4 Emotion Recognition Methods
4.1 Measuring Facial Expressions
4.2 Speech, Posture, and Gait Recognition
4.3 Measuring Physiological Indices for Emotion Recognition
5 Affective Disorders and Emotion-Related Deficits
6 Emotion Sensing Technologies in Emotion Disorders
6.1 SA in Affective Disorders
6.2 Computational Models and Machine Learning Techniques
6.3 Multimodal Emotion Recognition Methods in Depression
7 Humanizing Affective Computing and IoT
8 Affective Computing: Concerns, Challenges, and Future Directions
9 Conclusion
References
