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Artificial intelligence and machine learning for healthcare : image and data analytics / / edited by Chee Peng Lim, [and four others]
| Artificial intelligence and machine learning for healthcare : image and data analytics / / edited by Chee Peng Lim, [and four others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (239 pages) |
| Disciplina | 060 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico | Artificial intelligence - Medical applications |
| ISBN | 3-031-11154-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 An Introduction to Artificial Intelligence in Healthcare -- 1.1 Introduction to Artificial Intelligence -- 1.2 Artificial Intelligence in Healthcare -- 1.2.1 Natural Language Processing (NLP) Technology -- 1.2.2 Machine Learning (ML) Algorithms -- 1.2.3 Artificial Neural Networks -- 1.2.4 Bayesian Classifier -- 1.2.5 Classification/Decision Trees. Random Forest -- 1.2.6 Survival Regression Models -- 1.2.7 Cluster Analysis -- 1.3 Advantages of Artificial Intelligence in Healthcare -- 1.4 Limitations of Artificial Intelligence in Healthcare -- 1.5 Successful Applications of Artificial Intelligence in Healthcare -- 1.6 Conclusions -- Appendix -- Books -- 2 Radiomics: Approach to Precision Medicine -- 2.1 Introduction -- 2.2 Materials and Methods -- 2.2.1 Building of a Database -- 2.2.2 Segmentation of Target Volume -- 2.2.3 Extraction and Selection of Useful Radiomics Features -- 2.2.4 Model Building Based on Machine Learning Technologies -- 2.3 Results and Discussion -- 2.4 Conclusions -- References -- 3 Artificial Intelligence Based Strategies for Data-Driven Radial MRI -- 3.1 Introduction -- 3.2 Related Work -- 3.2.1 Sparse Sampling Strategies -- 3.2.2 Contribution of the Manuscript -- 3.3 Problem Statement and Framework Description -- 3.3.1 Relationship Between Radial Projections and Image -- 3.3.2 Image Reconstruction, Resolution and Noise -- 3.3.3 Super-Resolution -- 3.3.4 Framework Details -- 3.3.5 Noise Threshold upper TT -- 3.4 Results and Discussion -- 3.5 Conclusion -- References -- 4 Unsupervised Domain Adaptation Approach for Liver Tumor Detection in Multi-phase CT Images -- 4.1 Introduction -- 4.1.1 Domain-Shift Problem -- 4.1.2 Domain Adaptation -- 4.2 Domain Adaptation Using Adversarial Learning -- 4.2.1 Anchor-free Detector.
4.2.2 Proposed Multi-phase Domain Adaptation Framework Using Adversarial Domain Classification Loss -- 4.3 Proposed Multi-phase Domain Adaptation Framework Using Adversarial Learning with Maximum Square Loss -- 4.3.1 Maximum Square Loss -- 4.3.2 Overall Framework with Adversarial Domain Classification and Maximum Square Loss -- 4.4 Experiments -- 4.4.1 Implementation Details -- 4.4.2 Dataset -- 4.4.3 Evaluation -- 4.4.4 Results -- 4.5 Conclusions -- References -- 5 Multi-stage Synthetic Image Generation for the Semantic Segmentation of Medical Images -- 5.1 Introduction -- 5.2 Related Works -- 5.2.1 Synthetic Image Generation -- 5.2.2 Image-to-Image Translation -- 5.2.3 Retinal Image Synthesis and Segmentation -- 5.2.4 Chest X-ray Image Synthesis and Segmentation -- 5.3 Multi-stage Image Synthesis -- 5.3.1 Image Generation -- 5.4 Evaluation of Multi-stage Methods -- 5.4.1 Datasets -- 5.4.2 Segmentation Network -- 5.4.3 Experimental Setup -- 5.4.4 Two-Stage Method Evaluation -- 5.4.5 Three-Stage Method Evaluation -- 5.5 Conclusions -- References -- 6 Classification of Arrhythmia Signals Using Hybrid Convolutional Neural Network (CNN) Model -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Methodology -- 6.4 Results and Discussion -- 6.5 Conclusions -- Appendix 1 -- Appendix 2 -- Appendix 3 -- References -- 7 Polyp Segmentation with Deep Ensembles and Data Augmentation -- 7.1 Introduction -- 7.2 Related Methods -- 7.2.1 Overview of the Propose System -- 7.2.2 Loss Functions -- 7.3 Data Augmentation -- 7.3.1 Shadows -- 7.3.2 Contrast and Motion Blur -- 7.3.3 Color Mapping -- 7.4 Experimental Results -- 7.4.1 Data and Testing Protocol -- 7.4.2 Experiments -- 7.5 Conclusions -- References -- 8 Autistic Verbal Behavior Parameters -- 8.1 Introduction -- 8.2 Estate of the Art -- 8.3 Proposal, Materials and Methods -- 8.4 Testing Protocol. 8.5 Analysis of Tests -- 8.6 Conclusions and Future Work -- References -- 9 Advances in Modelling Hospital Medical Wards -- 9.1 Introduction and Problem Addressed -- 9.2 Case Study and Data Analysis -- 9.3 Methodology and Results -- 9.4 Conclusion -- References -- 10 Tracking Person-Centred Care Experiences Alongside Other Success Measures in Hearing Rehabilitation -- 10.1 Person-Centred Care in Research and Practice -- 10.1.1 Situated Action-Understanding the Context as a Basis for Meaningful Measures -- 10.1.2 Situated AI for Achieving High-Quality Person-Centred Care -- 10.2 Co-design for Person-Centred Care Measures -- 10.2.1 Co-design of Evaluation Instruments -- 10.2.2 Artificial Intelligence and PCC -- 10.3 Case Study: Co-creation of PCC Measures and Dashboard with Hearing Rehabilitation Provider -- 10.3.1 Method -- 10.4 Results -- 10.4.1 Stakeholder Workshops-Development of Tools -- 10.4.2 Stakeholder Feedback -- 10.4.3 Piloting the Dashboard -- 10.5 Discussion -- 10.5.1 Summary of Case Study -- 10.5.2 Discussion on Opportunities and Challenges for AI -- 10.5.3 Quality of Data -- 10.6 Conclusions -- References -- 11 BioGNN: How Graph Neural Networks Can Solve Biological Problems -- 11.1 Overview of the Research Area -- 11.1.1 Biological Problems on Graphs -- 11.1.2 Deep Learning Models for Biological Graphs -- 11.2 Graph Neural Networks -- 11.2.1 The Graph Neural Network Model -- 11.2.2 Composite Graph Neural Networks -- 11.2.3 Layered Graph Neural Networks -- 11.2.4 Approximation Power of Graph Neural Networks -- 11.2.5 Software Implementation -- 11.3 Biological Applications -- 11.3.1 Prediction of Protein-Protein Interfaces -- 11.3.2 Drug Side-Effect Prediction -- 11.3.3 Molecular Graph Generation -- 11.4 Conclusions and Future Perspectives -- References. |
| Record Nr. | UNINA-9910627260103321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence and machine learning for healthcare . Volume 2 : emerging methodologies and trends / / edited by Chee Peng Lim [and four others]
| Artificial intelligence and machine learning for healthcare . Volume 2 : emerging methodologies and trends / / edited by Chee Peng Lim [and four others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (282 pages) |
| Disciplina | 006.31 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Machine learning
Artificial intelligence - Medical applications |
| ISBN | 3-031-11170-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- 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. |
| Record Nr. | UNINA-9910627240503321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of Artificial Intelligence in Healthcare : Vol 2: Practicalities and Prospects / / edited by Chee-Peng Lim, Yen-Wei Chen, Ashlesha Vaidya, Charu Mahorkar, Lakhmi C. Jain
| Handbook of Artificial Intelligence in Healthcare : Vol 2: Practicalities and Prospects / / edited by Chee-Peng Lim, Yen-Wei Chen, Ashlesha Vaidya, Charu Mahorkar, Lakhmi C. Jain |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (429 pages) : illustrations (some color) |
| Disciplina | 610.285 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computational intelligence
Biomedical engineering Artificial intelligence Medical informatics Computational Intelligence Biomedical Engineering and Bioengineering Artificial Intelligence Health Informatics |
| ISBN | 3-030-83620-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Intelligent paradigms for diagnosis, prediction and control in healthcare applications -- Artificial intelligence in healthcare practice: How to tackle the “human” challenge -- A statistical analysis handbook for validating artificial intelligence techniques applied in healthcare. |
| Record Nr. | UNINA-9910523768003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||