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Applied Computing in Medicine and Health

Book Description

Applied Computing in Medicine and Health is a comprehensive presentation of on-going investigations into current applied computing challenges and advances, with a focus on a particular class of applications, primarily artificial intelligence methods and techniques in medicine and health.

Applied computing is the use of practical computer science knowledge to enable use of the latest technology and techniques in a variety of different fields ranging from business to scientific research. One of the most important and relevant areas in applied computing is the use of artificial intelligence (AI) in health and medicine. Artificial intelligence in health and medicine (AIHM) is assuming the challenge of creating and distributing tools that can support medical doctors and specialists in new endeavors. The material included covers a wide variety of interdisciplinary perspectives concerning the theory and practice of applied computing in medicine, human biology, and health care.

Particular attention is given to AI-based clinical decision-making, medical knowledge engineering, knowledge-based systems in medical education and research, intelligent medical information systems, intelligent databases, intelligent devices and instruments, medical AI tools, reasoning and metareasoning in medicine, and methodological, philosophical, ethical, and intelligent medical data analysis.

  • Discusses applications of artificial intelligence in medical data analysis and classifications
  • Provides an overview of mobile health and telemedicine with specific examples and case studies
  • Explains how behavioral intervention technologies use smart phones to support a patient centered approach
  • Covers the design and implementation of medical decision support systems in clinical practice using an applied case study approach

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. List of Contributors
  6. Editor Biographies
  7. Author Biographies
  8. Acknowledgment
  9. Introduction
  10. Chapter 1. Early Diagnosis of Neurodegenerative Diseases from Gait Discrimination to Neural Synchronization
    1. Introduction
    2. Research Challenges
    3. Neurodegenerative Diseases
    4. Classification Algorithms for NDDs
    5. Neural Synchronization and Data Collection
    6. Neural Synchrony Measurement Technique
    7. Data Description and Data Filtering
    8. Different Approaches to Compute EEG Synchrony
    9. Statistical Analysis
    10. Conclusion
  11. Chapter 2. Lifelogging Technologies to Detect Negative Emotions Associated with Cardiovascular Disease
    1. Introduction
    2. Background
    3. Research Challenges
    4. Summary
  12. Chapter 3. Gene Selection Methods for Microarray Data
    1. Introduction to Gene Selection
    2. Feature Selection Algorithms Based on Wrapper Approach
    3. Unsupervised Filter Based Feature Selection Algorithms
    4. Conclusions
  13. Chapter 4. Brain MRI Intensity Inhomogeneity Correction Using Region of Interest, Anatomic Structural Map, and Outlier Detection
    1. Introduction
    2. Mathematical Models of Bias Field
    3. Design Techniques of Current Algorithms
    4. Importance of Current Algorithms
    5. Methods
    6. Experimental Design
    7. Discussion
    8. Conclusion
  14. Chapter 5. Leveraging Big Data Analytics for Personalized Elderly Care: Opportunities and Challenges
    1. Introduction
    2. The Challenge of Personalization in Elderly Care
    3. Big Data Analytics for Elderly Care
    4. Proposed Framework
    5. Example Scenario
    6. Discussion and Conclusion
  15. Chapter 6. Prediction of Intrapartum Hypoxia from Cardiotocography Data Using Machine Learning
    1. Introduction
    2. Monitoring Intrapartum Fetal Hypoxia
    3. Ambulatory CTG Monitoring
    4. Proposed Methodology
    5. Future Research Directions
    6. Conclusions
  16. Chapter 7. Recurrent Neural Networks in Medical Data Analysis and Classifications
    1. Introduction
    2. Medical Data Preprocessing
    3. Classification
    4. RNNs for Classification
    5. Introduction to Preterm
    6. Electrohysterogram
    7. Uterine EHG Signal Processing
    8. Modeling RNN for Forecasting
    9. Conclusion
  17. Chapter 8. Assured Decision and Meta-Governance for Mobile Medical Support Systems
    1. Introduction
    2. Related Work and Literature Review
    3. A Calculus of Situations for Mobile Decision Support Systems Assurance
    4. Implementation Strategy
    5. Case Study
    6. Conclusions and Future Work
  18. Chapter 9. Identifying Preferences and Developing an Interactive Data Model and Assessment for an Intelligent Mobile Application to Manage Young Patients Diagnosed with Hydrocephalus
    1. Introduction
    2. Mobile Computing and Current Trends
    3. Mobile Technologies in Health Care
    4. Current Practices in Managing Patients with Hydrocephalus
    5. Development Methodology
    6. Findings
    7. The Intelligent NeuroDiary Application System (iNAS)
    8. Conclusion
  19. Chapter 10. Sociocultural and Technological Barriers Across all Phases of Implementation for Mobile Health in Developing Countries
    1. Introduction
    2. Mobile Health Implementation
    3. Methodology
    4. Potential Barriers to mHealth Implementation in Developing Countries
    5. Discussion and Findings
    6. Conclusion
  20. Chapter 11. Application of Real-Valued Negative Selection Algorithm to Improve Medical Diagnosis
    1. Introduction
    2. Related Work
    3. Biological Immune System
    4. Artificial Immune System
    5. Negative Selection Algorithm
    6. Experimental Investigation and Classification Results
    7. Conclusion
  21. Chapter 12. Development and Applications of Mobile Farming Information System for Food Traceability in Health Management
    1. Food Traceability for Safety and Health
    2. Development of Mobile Farming Information System
    3. Applications of the Mobile Farming Information System
    4. Summary and Conclusions
  22. Chapter 13. Telehealth in Primary Health Care: Analysis of Liverpool NHS Experience
    1. Introduction
    2. Method
    3. Analysis and Result
    4. Conclusion and Recommendations
  23. Chapter 14. Swarm Based-Artificial Neural System for Human Health Data Classification
    1. Introduction
    2. Issues on Human Health
    3. Related Works on Human Health
    4. Swarm-Based Artificial Neural System
    5. The Proposed Learning Algorithms
    6. Classification of Health Data Using the Proposed Approach
    7. Experimental Design
    8. Simulation Results on Health Data Classification
    9. Conclusion
  24. Index