Artificial intelligence and machine learning for healthcare . Volume 2 : emerging methodologies and trends / / edited by Chee Peng Lim [and four others]

• Pubblicazione: Cham, Switzerland : , : Springer, , [2023]; ©2023
• Descrizione fisica: 1 online resource (282 pages)
• Disciplina: 006.31
• Soggetto topico: Machine learning; Artificial intelligence - Medical applications
• Persona (resp. second.): CheePeng Lim
• 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.
• Titolo autorizzato: Artificial intelligence and machine learning for healthcare
• ISBN: 3-031-11170-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910627240503321
• Lo trovi qui: Univ. Federico II
• Serie: Intelligent Systems Reference Library