Bøger i Health Information Science serien

Artificial intelligent systems, which offer great improvement in healthcare sector assisted by machine learning, wireless communications, data analytics, cognitive computing, and mobile computing provide more intelligent and convenient solutions and services. With the help of the advanced techniques, now a days it is possible to understand human body and to handle & process the health data anytime and anywhere. It is a smart healthcare system which includes patient, hospital management, doctors, monitoring, diagnosis, decision making modules, disease prevention to meet the challenges and problems arises in healthcare industry. Furthermore, the advanced healthcare systems need to upgrade with new capabilities to provide human with more intelligent and professional healthcare services to further improve the quality of service and user experience. To explore recent advances and disseminate state-of-the-art techniques related to intelligent healthcare services and applications. This edited book involved in designing systems that will permit the societal acceptance of ambient intelligence including signal processing, imaging, computing, instrumentation, artificial intelligence, internet of health things, data analytics, disease detection, telemedicine, and their applications. As the book includes recent trends in research issues and applications, the contents will be beneficial to Professors, researchers, and engineers. This book will provide support and aid to the researchers involved in designing latest advancements in communication and intelligent systems that will permit the societal acceptance of ambient intelligence. This book presents the latest research being conducted on diverse topics in intelligence technologies with the goal of advancing knowledge and applications healthcare sector and to present the latest snapshot of the ongoing research as well as to shed further light on future directions in this space. The aim of publishing the book is to serve for educators, researchers, and developers working in recent advances and upcoming technologies utilizing computational sciences.

1 Paradigms in Epidemiology1.1 Methodological Paradigms1.2 Recent Developments1.3 Infectious Diseases and Vaccination 1.4 Objectives and Tasks 1.4.1 Modeling Infectious Disease Dynamics 1.4.2 Modeling Vaccine Allocation Strategies 1.4.3 Modeling Vaccination Decision-Making 1.4.4 Modeling Subjective Perception 1.5 Summary 2 Computational Modeling in a Nutshell2.1 Modeling Infectious Disease Dynamics 2.1.1 Infectious Disease Models 2.1.2 Age-Specific Disease Transmissions2.2 Modeling Contact Relationships 2.2.1 Empirical Methods 2.2.2 Computational Methods2.3 Case Study 2.3.1 2009 Hong Kong H1N1 Influenza Epidemic 2.3.2 Age-Specific Contact Matrices 2.3.3 Validation2.4 Further Remarks 2.5 Summary3 Strategizing Vaccine Allocation3.1 Vaccination3.1.1 Herd Immunity 3.1.2 Vaccine Allocation Strategy3.2 Vaccination Priorities 3.3 Age-Specific Intervention Priorities 3.3.1 Modeling Prioritized Interventions 3.3.2 Effects of Vaccination 3.3.3 Effects of Contact Reduction3.3.4 Integrated Measures 3.4 Case Study 3.4.1 2009 Hong Kong HSI Vaccination Programme 3.4.2 Effects of Prioritized Interventions3.5 Further Remarks3.6 Summary4 Explaining Individuals'' Vaccination Decisions4.1 Costs and Benefits for Decision-Making4.2 Game-Theoretic Modeling of Vaccination Decision-Making4.3 Case Study 4.3.1 2009 Hong Kong HSI Vaccination Programme4.3.2 Vaccination Coverage 4.4 Further Remarks4.5 Summary 5 Characterizing Socially Influenced Vaccination Decisions 5.1 Social Influences on Vaccination Decision-Making 5.2 Case Study 5.2.1 Vaccination Coverage 5.3 Further Remarks5.4 Summary 6 Understanding the Effect of Social Media 6.1 Modeling Subjective Perception 6.2 Subjective Perception in Vaccination Decision-Making 6.2.1 Dempster-Shafer Theory (DST)6.2.2 Spread of Social Awareness 6.3 Case Study 6.3.1 Vaccination Decision-Making in an Online SocialCommunity6.3.2 Interplay of Two Dynamics 6.4 Further Remarks 6.5 Summary7 Welcome to the Era of Systems Epidemiology 7.1 Systems Thinking in Epidemiology 7.2 Systems Modeling in Principle7.3 Systems Modeling in Practice7.4 Toward Systems Epidemiology 8 Further Readings References 

Introducing an innovative concept for integrating social media data with clinical data, it addresses the crucial aspect of combining experiential data from social media with clinical evidence, and explores how the variety of available social media content can be analyzed and implemented.

This book presents advances in biomedical imaging analysis and processing techniques using time dependent medical image datasets for computer aided diagnosis.

This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems.

This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection.With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.

Healthcare service systems are of profound importance in promoting the public health and wellness of people. This book introduces a data-driven complex systems modeling approach (D2CSM) to systematically understand and improve the essence of healthcare service systems. In particular, this data-driven approach provides new perspectives on health service performance by unveiling the causes for service disparity, such as spatio-temporal variations in wait times across different hospitals.The approach integrates four methods -- Structural Equation Modeling (SEM)-based analysis; integrated projection; service management strategy design and evaluation; and behavior-based autonomy-oriented modeling -- to address respective challenges encountered in performing data analytics and modeling studies on healthcare services. The thrust and uniqueness of this approach lies in the following aspects:Ability to explore underlying complex relationships between observed or latent impact factors and service performance.Ability to predict the changes and demonstrate the corresponding dynamics of service utilization and service performance.Ability to strategically manage service resources with the adaptation of unpredictable patient arrivals.Ability to figure out the working mechanisms that account for certain spatio-temporal patterns of service utilization and performance.To show the practical effectiveness of the proposed systematic approach, this book provides a series of pilot studies within the context of cardiac care in Ontario, Canada. The exemplified studies have unveiled some novel findings, e.g., (1) service accessibility and education may relieve the pressure of population size on service utilization; (2) functionally coupled units may have a certain cross-unit wait-time relationship potentially because of a delay cascade phenomena; (3) strategically allocating time blocks in operating rooms (ORs) based on a feedback mechanism may benefit OR utilization; (4) patients' and hospitals' autonomous behavior, and their interactions via wait times may bear the responsible for the emergence of spatio-temporal patterns observed in the real-world cardiac care system. Furthermore, this book presents an intelligent healthcare decision support (iHDS) system, an integrated architecture for implementing the data-driven complex systems modeling approach to developing, analyzing, investigating, supporting and advising healthcare related decisions.In summary, this book provides a data-driven systematic approach for addressing practical decision-support problems confronted in healthcare service management. This approach will provide policy makers, researchers, and practitioners with a practically useful way for examining service utilization and service performance in various ``what-if" scenarios, inspiring the design of effectiveness resource-allocation strategies, and deepening the understanding of the nature of complex healthcare service systems.

This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection.With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.