Books in the Health Information Science series

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.

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.

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.

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