http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Computational intelligence in biomedical tnternet of medical things
Through comprehensive insights and real-world case studies, this book features in-depth knowledge of key concepts relating to optimizing biomedical IoMT systems.Biomedical Internet of Medical Things is a technological paradigm encompassing a range of technologies that enable machines to mimic human intelligence.
Cover -- Series Page -- Title Page -- Copyright Page -- Preface -- Part I: Foundations and Frameworks -- Chapter 1 Introduction to Computational Biomedical Intelligence -- 1.1 Introduction -- 1.2 Emergence of Computational Biomedical Intelligence -- 1.3 Applications Driving Innovation in Healthcare -- 1.4 Bridging Biomedical Complexity with Advanced Computing -- 1.5 Prospects and Challenges -- 1.6 Conclusion -- References -- Chapter 2 Fundamentals of Biomedical Data and Social Determinants -- 2.1 Introduction -- 2.2 Methodology -- 2.2.1 Dataset -- 2.2.2 Preprocessing -- 2.3 Models of Machine Learning and Deep Learning are Employed to Predict Cardiovascular Diseases -- 2.4 Results -- 2.5 Conclusion -- References -- Chapter 3 The Impact of Biomedical Intelligence on Public Health -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Proposed Methods -- 3.3.1 Data Collection -- 3.4 Challenges the Biomedical Intelligence in Public Healthcare -- 3.5 Impact of Public Health on Disease Prediction Using Machine Learning -- 3.6 Impact of Public Health in Disease Prediction Using Deep Learning -- 3.7 Result and Discussion -- 3.8 Discussion -- 3.9 Conclusion -- References -- Chapter 4 Healthcare Data Integration and Management -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Selection Criteria -- 4.2.1.1 Inclusion Criteria -- 4.2.1.2 Exclusion Criteria -- 4.2.2 Challenges and Benefits of Healthcare Data Integration -- 4.3 Results -- 4.3.1 Discussion -- 4.4 Conclusion -- References -- Chapter 5 Ethics and Legal Issues in Computational Biomedical Intelligence -- 5.1 Introduction -- 5.2 Description of Biomedical Technological Growth and Development -- 5.2.1 Advancement and Obstacles of Biomedical Innovation -- 5.2.2 Identification and Aspects of Modern Biomedical Technologies -- 5.2.3 The Application of Ethical Principles in Biomedical Science and Technology.
5.3 Ethical Considerations in Computational Biomedical Intelligence -- 5.3.1 Ethical Perspective -- 5.3.2 Ethical and Regulatory Considerations -- 5.3.3 Principles of Biomedical Ethics in the Use of Artificial Intelligence in Medical Education -- 5.3.4 Self-Determination -- 5.3.5 Equality -- 5.3.6 Beneficence -- 5.3.7 Lack of Malice -- 5.4 Legal Considerations in Computational Biomedical Intelligence -- 5.4.1 Legal Control and Biomedical Moral Policy -- 5.4.2 Strategies for Legal Effects on the Advancement of Biomedical Technologies -- 5.5 Result -- 5.6 Discussion -- 5.7 Conclusion -- References -- Part II: Advanced Analytical Techniques -- Chapter 6 Bioinstrumentation and Medical Devices -- 6.1 Introduction -- 6.2 Computational Intelligence Techniques in the Internet of Medical Things -- 6.2.1 Using Computational Intelligence Techniques in Bioinstrumentation -- 6.2.1.1 The Role of Artificial Intelligence in Establishing a Smart Sensor Network -- 6.2.2 Applications of CI Across Devices -- 6.2.2.1 Remote Patient Monitoring -- 6.2.2.2 IoMT-Enabled Devices -- 6.2.3 Effective Use of CI to Enhance Bioinstrumentation and Medical Equipment -- 6.2.3.1 Deep Learning for Diagnostics -- 6.2.3.2 Remote Patient Monitoring with IoMT -- 6.2.4 Utilizing CI in Medical Devices and Bioinstrumentation -- 6.2.4.1 Diagnostic Applications (Imaging Systems) -- 6.2.4.2 Applications in Monitoring -- 6.2.5 Limitations and Difficulties -- 6.2.5.1 Ethical and Regulatory Concerns -- 6.2.5.2 Data Quality and Security Issues in IoMT Systems -- 6.2.5.3 Integration of CI with Existing Medical Infrastructure -- 6.2.6 Prospects and Patterns for the Future -- 6.2.6.1 Heavy Dependence on Edge Computing -- 6.2.6.2 5G-Connected Real-Time Monitoring Device -- 6.2.6.3 Potential for Next-Generation Bioinstrumentation Devices Enhanced by CI.
6.2.7 Artificial Intelligent (AI)-Based Diagnostic Applications -- 6.3 Conclusion -- References -- Chapter 7 Biomedical Signal Processing for Community Health -- 7.1 Introduction -- 7.1.1 Contributions -- 7.2 Literature Reviews -- 7.3 Methodology -- 7.3.1 Data Collection -- 7.3.2 Preprocessing Using Min-Max Scaler -- 7.3.3 Feature Extraction Using Wavelet Transform -- 7.3.4 Biomedical Signal Processing for Community Health Using White Shark Optimized Refined Random Forest -- 7.3.4.1 Refined Random Forest -- 7.3.4.2 White Shark Optimization -- 7.4 Results -- 7.4.1 Accuracy -- 7.4.2 Root Mean Squared Error -- 7.4.3 Mean Absolute Error -- 7.5 Discussion -- 7.6 Conclusion -- References -- Chapter 8 Medical Image Processing and Analysis -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Methodology -- 8.3.1 Data Collection -- 8.3.2 Data Preprocessing Using Denoising -- 8.3.3 Image Segmentation Using k-Means Clustering -- 8.3.4 Feature Extraction Using a Convolutional Neural Network -- 8.3.5 Medical Image Processing and Analysis Using Enriched Whale-Optimized Logistic Regression -- 8.3.5.1 Logistic Regression -- 8.3.5.2 Enriched Whale Optimized -- 8.4 Result -- 8.4.1 F1-Score -- 8.5 Discussion -- 8.6 Conclusion -- References -- Chapter 9 Genomic and Proteomic Data Analysis for Population Health -- 9.1 Introduction -- 9.2 Methodology -- 9.2.1 Genomes -- 9.2.2 Proteomic -- 9.2.3 Sequence of Important DNA -- 9.3 Human Genome Project -- 9.3.1 Human Genetic Variation -- 9.3.2 The Significance of Protein Structure -- 9.3.3 Database -- 9.3.4 Types of Databases -- 9.3.5 Difference between Nucleic Acid and Protein Database -- 9.3.6 Developments in Throughput Technology -- 9.4 Information Synthesis and Integration -- 9.4.1 Unstructured and Non-Standard Data -- 9.4.2 Processing Complex Queries -- 9.4.3 Integration of Data from Various Related Databases.
9.4.4 Needed Features vs Current Capabilities -- 9.4.5 Key Areas of Focus for Data Management Study -- 9.4.6 Data Management Solutions -- 9.4.7 Proteomic and Genomic Data Analysis -- 9.5 Conclusion -- References -- Chapter 10 Telemedicine and Remote Monitoring -- 10.1 Introduction -- 10.2 Related Works -- 10.3 Methodology -- 10.3.1 Dataset Collection -- 10.3.2 Remote Monitoring Using ML -- 10.4 Experimental Results -- 10.4.1 Performance Metrics -- 10.5 Discussion -- 10.6 Conclusion -- References -- Chapter 11 Machine Learning and Artificial Intelligence in Biomedical Applications -- 11.1 Introduction -- 11.2 Levels of Artificial Intelligence Applications Across Biomedical Subfields -- 11.3 Artificial Intelligence-Based Medical Imaging -- 11.3.1 Cardiovascular Health -- 11.3.2 Neurological Condition -- 11.3.3 Cancer Detection -- 11.3.4 Brain Tumor Identification -- 11.3.5 Muscle Injury Identification -- 11.3.6 Radiation Dosage -- 11.4 Different Diagnosis Using Machine Learning and Artificial Intelligence -- 11.5 Performance Evaluation of Machine Learning and Deep Learning Methods in Biomedical Application -- 11.6 Transformative Impact of Artificial Intelligence in Healthcare -- 11.7 Conclusion -- References -- Chapter 12 The Internet of Medical Things in Chronic Disease Management -- 12.1 Introduction -- 12.1.1 Contributions of this Research -- 12.2 Related Works -- 12.3 Methodology -- 12.3.1 Data Collection -- 12.3.2 Preprocessing Using Min-Max Normalization -- 12.3.3 Feature Extraction Using Principal Component Analysis -- 12.3.4 Fine-Tuned Butterfly-Optimized Random Forest -- 12.3.5 Random Forest -- 12.3.6 Fine-Tuned Butterfly Optimization -- 12.4 Results -- 12.5 Discussion -- 12.6 Conclusion -- 12.6.1 Limitations and Future Research -- References -- Chapter 13 Wearable Technologies in Internet of Medical Things Biomedical Intelligence.
13.1 Introduction -- 13.2 Related Articles -- 13.3 Methodology -- 13.3.1 Data Collection -- 13.3.2 Preprocessing -- 13.3.3 Feature Extraction -- 13.3.4 Intelligent Dwarf Mongoose Fine-Tuned Resilient LightGBM -- 13.3.5 Resilient LightGBM -- 13.3.6 Intelligent Dwarf Mongoose -- 13.4 Result and Discussion -- 13.4.1 Discussion -- 13.5 Conclusion -- 13.5.1 Limitation and Future Scope -- References -- Part III: Systems and Applications -- Chapter 14 Health Informatics and Community Decision Support Systems -- 14.1 Introduction -- 14.2 Methodology -- 14.3 Patient-Centered Care through Healthcare Informatics -- 14.4 Clinical Decision Support Systems Framework -- 14.4.1 Knowledge Base -- 14.5 Telemedicine Framework -- 14.6 Telehealth Framework -- 14.7 Conclusion -- References -- Chapter 15 Healthcare Data Acquisition and Management -- 15.1 Introduction -- 15.2 Healthcare Data Framework -- 15.2.1 Data Acquisition -- 15.2.2 Data Preprocessing -- 15.2.3 Data Storage -- 15.2.4 Data Management -- 15.2.5 Data Analytics -- 15.2.6 Data Visualization -- 15.3 Assessment of Healthcare Data -- 15.3.1 Accuracy -- 15.4 Conclusion -- References -- Chapter 16 Telemedicine and Remote Health Monitoring for Underserved Communities -- 16.1 Introduction -- 16.2 Accessing Community Needs and Building Awareness -- 16.2.1 Community Input Required -- 16.2.2 Building Awareness -- 16.3 Establishing Infrastructure and Ensuring Accessibility -- 16.3.1 Electronic Medical Records System -- 16.3.2 Electronic Health Record -- 16.3.3 Personal Health of Records -- 16.3.4 Data Interchange Capabilities -- 16.4 Implementing Remote Health Monitoring and Device Distribution -- 16.5 Facilitating Telemedicine and Providing Ongoing Support -- 16.5.1 Telemedicine is Necessary for the Healthcare System.
Satya Prakash Yadav, Abhay Bansal, and Victor Hugo C. de Albuquerque.
16.5.2 Telemedicine's Features and Capabilities When Integrated into a Healthcare Management System.
Computational Intelligence in Biomedical Internet of Medical Things
Newark : John Wiley & Sons, Incorporated,c2026