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chee l ai machine learning healthcare vol 1 2022

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Textbook in PDF format Artificial intelligence (AI) and machine learning (ML) have transformed many standard and conventional methods in undertaking health and well-being issues of humans. AL/ML-based systems and tools play a critical role in this digital and big data era to address a variety of medical and healthcare problems, improving treatments and quality of care for patients. This edition on AI and ML for healthcare consists of two volumes. The first presents selected AI and ML studies on medical imaging and healthcare data analytics, while the second unveils emerging methodologies and trends in AI and ML for delivering better medical treatments and healthcare services in the future. In this first volume, progresses in AI and ML technologies for medical image, video, and signal processing as well as health information and data analytics are presented. These selected studies offer readers theoretical and practical knowledge and ideas pertaining to recent advances in AI and ML for effective and efficient image and data analytics, leading to state-of-the-art AI and ML technologies for advancing the healthcare sector. Preface An Introduction to Artificial Intelligence in Healthcare Introduction to Artificial Intelligence Artificial Intelligence in Healthcare Natural Language Processing (NLP) Technology Machine Learning (ML) Algorithms Artificial Neural Networks Bayesian Classifier Classification/Decision Trees Random Forest Survival Regression Models Cluster Analysis Advantages of Artificial Intelligence in Healthcare Limitations of Artificial Intelligence in Healthcare Successful Applications of Artificial Intelligence in Healthcare Conclusions Appendix Books Radiomics: Approach to Precision Medicine Introduction Materials and Methods Building of a Database Segmentation of Target Volume Extraction and Selection of Useful Radiomics Features Model Building Based on Machine Learning Technologies Results and Discussion Conclusions References Artificial Intelligence Based Strategies for Data-Driven Radial MRI Introduction Related Work Sparse Sampling Strategies Contribution of the Manuscript Problem Statement and Framework Description Relationship Between Radial Projections and Image Image Reconstruction, Resolution and Noise Super-Resolution Framework Details Noise Threshold upper TT Results and Discussion Conclusion References Unsupervised Domain Adaptation Approach for Liver Tumor Detection in Multi-phase CT Images Introduction Domain-Shift Problem Domain Adaptation Domain Adaptation Using Adversarial Learning Anchor-free Detector Proposed Multi-phase Domain Adaptation Framework Using Adversarial Domain Classification Loss Proposed Multi-phase Domain Adaptation Framework Using Adversarial Learning with Maximum Square Loss Maximum Square Loss Overall Framework with Adversarial Domain Classification and Maximum Square Loss Experiments Implementation Details Dataset Evaluation Results Conclusions References Multi-stage Synthetic Image Generation for the Semantic Segmentation of Medical Images Introduction Related Works Synthetic Image Generation Image-to-Image Translation Retinal Image Synthesis and Segmentation Chest X-ray Image Synthesis and Segmentation Multi-stage Image Synthesis Image Generation Evaluation of Multi-stage Methods Datasets Segmentation Network Experimental Setup Two-Stage Method Evaluation Three-Stage Method Evaluation Conclusions References Classification of Arrhythmia Signals Using Hybrid Convolutional Neural Network (CNN) Model Introduction Literature Review Methodology Results and Discussion Conclusions Appendix Appendix Appendix References Polyp Segmentation with Deep Ensembles and Data Augmentation Introduction Related Methods Overview of the Propose System Loss Functions Data Augmentation Shadows Contrast and Motion Blur Color Mapping Experimental Results Data and Testing Protocol Experiments Conclusions References Autistic Verbal Behavior Parameters Introduction Estate of the Art Proposal, Materials and Methods Testing Protocol Analysis of Tests Conclusions and Future Work References Advances in Modelling Hospital Medical Wards Introduction and Problem Addressed Case Study and Data Analysis Methodology and Results Conclusion References Tracking Person-Centred Care Experiences Alongside Other Success Measures in Hearing Rehabilitation Person-Centred Care in Research and Practice Situated Action—Understanding the Context as a Basis for Meaningful Measures Situated AI for Achieving High-Quality Person-Centred Care Co-design for Person-Centred Care Measures Co-design of Evaluation Instruments Artificial Intelligence and PCC Case Study: Co-creation of PCC Measures and Dashboard with Hearing Rehabilitation Provider Method Results Stakeholder Workshops—Development of Tools Stakeholder Feedback Piloting the Dashboard Discussion Summary of Case Study Discussion on Opportunities and Challenges for AI Quality of Data Conclusions References BioGNN: How Graph Neural Networks Can Solve Biological Problems Overview of the Research Area Biological Problems on Graphs Deep Learning Models for Biological Graphs Graph Neural Networks The Graph Neural Network Model Composite Graph Neural Networks Layered Graph Neural Networks Approximation Power of Graph Neural Networks Software Implementation Biological Applications Prediction of Protein-Protein Interfaces Drug Side-Effect Prediction Molecular Graph Generation Conclusions and Future Perspectives References

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