Artificial Intelligence and Machine Learning for Healthcare: Vol. 2: Emerging Methodologies and Trends (Intelligent Systems Reference Library, 229)

✍ Scribed by Chee Peng Lim (editor), Ashlesha Vaidya (editor), Yen-Wei Chen (editor), Vaishnavi Jain (editor), Lakhmi C. Jain (editor)

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 282
• Edition: 1
• Category: Library

✦ Synopsis

In line with advances in digital and computing systems, artificial intelligence (AI) and machine learning (ML) technologies have transformed many aspects of medical and healthcare services, delivering tangible benefits to patents and the general public. This book is a sequel of the edition on “Artificial Intelligence and Machine Learning for Healthcare”. The first volume is focused on utilization of AI and ML for image and data analytics in the medical and healthcare domains. In this second volume, emerging methodologies and future trends in AI and ML for advancing medical treatments and healthcare services are presented. The selected studies in this book provide readers a glimpse on current progresses in AI and ML for undertaking a variety of healthcare-related tasks. The advances in AI and ML technologies for future healthcare are also discussed, shedding light on the potential of AI and ML to realize the next-generation medical treatments and healthcare services for the betterment of our global society.

✦ Table of Contents

Preface
Contents
1 Artificial Intelligence for the Future of Medicine
1.1 Introduction
1.2 How Do Machines Learn?
1.2.1 Machine Learning Process
1.2.2 Machine Learning in Medicine
1.3 Artificial Intelligence in Medicine
1.4 AI Applications in Medicine
1.4.1 Predictive Medicine
1.4.2 Participatory Medicine
1.4.3 Personalized Medicine
1.4.4 Preventive Medicine
1.5 Summary
References
2 A Survival Analysis Guide in Oncology
2.1 Introduction
2.2 Survival Analysis
2.3 Kaplan–Meier Survival Curve
2.4 The Logrank Test
2.5 The Hazard Ratio
2.5.1 Cox Regression Model
2.6 Conclusions
References
3 Social Media Sentiment Analysis Related to COVID-19 Vaccinations
3.1 Introduction
3.2 Literature Review
3.2.1 Machine Learning-Based Sentiment Analysis Studies
3.2.2 Lexicon-Based Sentiment Analysis Studies
3.2.3 Hybrid Sentiment Analysis Studies
3.3 Methodology
3.3.1 Methodology Outline
3.4 Experiments
3.4.1 Dataset
3.4.2 Dataset Pre-Processing
3.4.3 Sentiment Analysis
3.5 Experimental Results
3.6 Conclusion
3.6.1 Discussion
3.6.2 Overview of Contribution
3.6.3 Future Directions
References
4 Healthcare Support Using Data Mining: A Case Study on Stroke Prediction
4.1 Introduction
4.1.1 Data Mining
4.1.2 Data Mining in Healthcare
4.1.3 Applications of Data Mining in Healthcare
4.1.4 Chapter Overview
4.2 Literature Review
4.2.1 Data Mining Applications in Healthcare
4.2.2 Machine Learning Concepts Related with Healthcare Support
4.3 Methodology and Results
4.3.1 Methodology Outline
4.3.2 Experiments
4.4 Conclusion
4.4.1 Discussion
4.4.2 Issues and Challenges of Data Mining in Stroke Prediction and Healthcare
4.4.3 Future Directions and Insights
References
5 A Big Data Infrastructure in Support of Healthy and Independent Living: A Real Case Application
5.1 Introduction
5.2 Architecture
5.2.1 HomeHub
5.2.2 SB@App
5.2.3 Security Component
5.3 Clinical Interventions
5.3.1 Hearing Loss
5.3.2 Balance Disorders
5.4 Initial Implementation and Testing
5.4.1 Data Insights on the Platform
5.4.2 Data Insights on the SB@App
5.5 Data Analytics
5.6 Conclusion
References
6 Virtual Reality-Based Rehabilitation Gaming System
6.1 Introduction
6.1.1 Rehabilitation
6.1.2 Stroke
6.1.3 Musculoskeletal Disorders (MSDs)
6.2 Classical Treatment Approaches
6.2.1 Methods and Procedures for Stroke Treatment
6.2.2 Treatment Approaches for Musculoskeletal Disorders
6.2.3 Limitations in Traditional Approaches
6.3 Modern Rehabilitation Technologies
6.3.1 Physical Prosthetics
6.3.2 Sensory Prosthetics
6.3.3 Robotics Rehabilitation
6.3.4 Brain-Computer Interface (BCI)
6.4 Virtual-Reality (VR)
6.4.1 Classification of Virtual Reality Based on Experience
6.4.2 Virtual Reality Devices
6.4.3 VR in Rehabilitation
6.4.4 Game-Based VR Rehabilitation
6.4.5 System Requirements
6.4.6 System Architecture
6.5 VR Applications for Rehabilitation
6.5.1 Virtual Reality in Mental Rehabilitation
6.5.2 Autism
6.5.3 Cerebral Palsy
6.5.4 Upper Limb Prosthetic Training
6.5.5 Sports Rehabilitation Exercises
6.6 Summary and Conclusion
References
7 The Use of Serious Games for Developing Social and Communication Skills in Children with Autism Spectrum Disorders—Review
7.1 Introduction
7.2 Background
7.2.1 Autism Spectrum Disorder (ASD)
7.2.2 Application of Information and Communication Technologies in Therapy
7.2.3 Types of Technologies
7.2.4 Serious Games
7.3 Aim of the Study
7.4 Material and Methods
7.4.1 Relevant Research
7.5 Discussion
7.6 Conclusion
References
8 Deep Learning-based Coronary Stenosis Detection in X-ray Angiography Images: Overview and Future Trends
8.1 Introduction
8.2 Convolutional Neural Networks
8.3 Attention Mechanisms
8.4 Vision Transformers
8.5 Quantum Computing
8.6 Stenosis Detection Methods Based on Deep Learning
8.6.1 Object-based Classification
8.6.2 Image-Based Classification
8.7 Illustrative Study Cases
8.8 Challenges and Future Work
8.9 Conclusions
References
9 Potential Benefits of Artificial Intelligence in Healthcare
9.1 Introduction
9.2 Artificial Intelligence in Healthcare
9.3 Research Design
9.3.1 Systematic Literature Review (SLR)
9.3.2 Generation of Hypotheses and Conceptual Model
9.3.3 Data Collection
9.4 Results
9.4.1 Data Analysis and Sample Characteristics
9.4.2 Examination of Quality Criteria
9.4.3 Evaluation of the SEM
9.5 Interpretation
9.6 Recommended Activities: Cooperation and Exchange Between Different Stakeholders
9.7 Conclusion and Outlook
References
10 Barriers of Artificial Intelligence in the Health Sector
10.1 Introduction
10.2 Empirical Investigation
10.2.1 Research Design
10.2.2 Systematic Literature Review and Generation of Hypotheses
10.2.3 Data Collection
10.3 Results
10.3.1 Data Analysis
10.3.2 Empirical Findings and Model Conceptualization
10.4 Discussion
10.5 Limitations and Further Research
References