The rise of lifestyle changes resulting from constant connectivity, irregular work schedules, heightened stress, and disruptive sleep patterns, have contributed to increasing insomnia rates. Exacerbated by the COVID-19 pandemic, sleep disorders are more prevalent than ever. This handbook offers a comprehensive exploration of the fusion of Artificial Intelligence (AI) and data science within the realm of sleep disorders, presenting innovative approaches to diagnosis, treatment, and personalized care. The interdisciplinary nature of this handbook fosters collaboration between experts from diverse fields, including computer science, engineering, neuroscience, medicine, public health, AI, data science, and sleep medicine. Each chapter delves into specific aspects of sleep disorder analysis, innovative methodologies, novel insights, and real-world applications that showcase the transformative potential of AI and data science in sleep medicine, from analyzing sleep patterns and predicting disorder risk factors to utilizing big data analytics for large-scale epidemiological studies. This handbook hopes to offer a comprehensive resource for researchers, clinicians, and policymakers striving to address the challenges in sleep medicine.

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is highly prevalent and represents a growing global healthcare burden. Besides disrupting sleep, sleep-disordered breathing leads to detrimental outcomes such as excessive daytime sleepiness, neurocognitive impairment, and increased cardiometabolic morbidities. The gold standard diagnostic procedures (e.g., in-lab polysomnography) are cumbersome and standard parameters derived from sleep studies often yield poor long-term prognostic value. Hence, there is a need for innovative diagnostic technologies and novel sleep metrics to enable simple diagnosis and tailored disease management.

'Artificial Intelligence in Medicine' is a comprehensive guide exploring the transformative impact of artificial intelligence (AI) in healthcare. The book delves into the foundational concepts and historical development of AI in medicine, highlighting data collection, preprocessing, and feature extraction crucial for medical applications. It showcases the benefits of AI, such as accurate diagnoses and personalized treatments, while addressing ethical and regulatory considerations.The book examines the practical aspects of AI implementation in clinical practice and emphasizes the human aspect of AI in healthcare and patient engagement. Readers can gain insights into the role of AI in clinical decision support, collaborative learning, and knowledge sharing. It concludes with a glimpse into the future of AI-driven healthcare, exploring the emerging technologies and trends in the rapidly evolving field of AI in medicine.

There are more than 80 different sleep disorders including insomnia, sleep apnea, restless leg syndrome, hypersomnia, circadian rhythm disorders, and parasomnia. Good sleep is necessary for optimal health and can affect hormone levels and weight. The use of artificial intelligence (AI) and biomedical signals and images can help in healthcare diagnostics that are related to these and other sleep disorders. Advancing the Investigation and Treatment of Sleep Disorders Using AI presents an overview of sleep disorders based on machine intelligence methods in order to learn and explore the latest advancements, developments, methods, systems, futuristic approaches, and algorithms towards sleep disorders and to address their challenges. This book also discusses recent and future advancements in various feature extraction techniques and machine learning methods. Covering topics such as biomedical signal processing, augmented reality for clinical investigation, and sleep disorder detection, this book is essential for sleep medicine practitioners, clinical psychologists, psychiatrists, medical technologists, doctors, IT specialists, biomedical engineers, researchers, graduate students, and academicians.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

Instructional technologies used to be optional and supplemental pedagogical tools until the global health crisis of 2020 compelled education systems to rely on digital devices and services to guarantee academic continuity. Suddenly, the contemporary principles and practices utilized in delivering health education curricula were insufficient and ineffective. Acknowledging the vital role of technology in shaping the future of education, there is now a greater demand to foster innovative interventions and continuous improvement in strategies, methodologies, and systems to empower learners, educators, and leaders in the digital age. This paradigm shift requires a fundamental transformation in the way we approach teaching and learning, and a willingness to embrace new approaches and tools that can enhance the quality of education and support student success. The Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines provides comprehensive coverage of innovative methods and strategies to produce the next generation of health professionals. The book lays the groundwork for implementable teaching and learning models that facilitate knowledge acquisition, enhance perceptual variation, improve skill coordination, and develop a scientific and technological mindset. Each chapter provides an in-depth examination of instructional technologies contextualized in various medical and health domains, including nursing, physiotherapy, radiology, neurophysiology, physical health, dentistry, clinical medicine, and more. This reference work is a must-read for all stakeholders in health education and related fields, including educators, students, researchers, administrators, and healthcare professionals.

Clinical Neuropsychology is a vast and varied field that focuses on the treatment, assessment and diagnosis of a range of cognitive disorders through a study and understanding of neuroanatomy and the relationship between the brain and human behavior. This handbook focuses on specific Neuropsychological disorders. It covers each of the classification systems involved before moving on to specific types of disorders from neurodevelopmental to neuropathological as well as brain injuries, trauma and neurodegenerative disorders. This handbook not only provides an in-depth overview of these Neuropsychological disorders, but also explores the history of the field as well its global challenges. The handbook is an essential tool for clinicians and scientists, as well as postgraduate students and researchers in a range of disciplines exploring the area. PART I BACKGROUND CONSIDERATIONS PART II NEURODEVELOPMENTAL DISORDERS PART III NEUROLOGICAL DISORDERS PART IV NEUROCOGNITIVE DISORDERS PART V TRAUMATIC BRAIN INJURY PART VI PSYCHIATRIC DISORDERS

It is almost 120 years since Alzheimer’s disease (AD) was first reported, and the concept of modifiable risk factors associated with the disease has been present from the outset. Thus, the idea of preventing AD is not new, with reference to strategies noted as early as the 1990s. This subfield of AD research has matured in recent years, with the number of modifiable risk factors – the AD preventome – rising from the 7 initially identified to the current 12, with an estimated contribution to dementia cases worldwide of about 40%. This book, the Handbook of Prevention and Alzheimer’s Disease, introduces physicians, scientists, and other stakeholders to this subfield of AD research. It investigates the AD preventome, which will continue to expand as the understanding of new factors and related biomarkers is refined. Optimizing this preventome leads to an improvement in overall brain health, an outcome which reduces the risk of developing AD and improves quality of life. The book goes on to examine other domains of prevention, from vascular risk factors to social engagement and from sleep health to spirituality. If the journey to end AD can be likened to a long and arduous challenge, understanding every possible part of the overall toolkit of approaches for disease prevention and intervention is essential. Together with its companion volume on intervention, the book provides a comprehensive overview of strategies for tackling Alzheimer’s disease, and will be of interest to all those working in the field. Cover illustration: White matter tracts showing sex differences in connectivity in men versus women as a function of increasing body mass index. Reprinted with permission from Rahmani F, Wang Q, McKay NS, Keefe S, Hantler N, Hornbeck R, Wang Y, Hassenstab J, Schindler S, Xiong C, Morris JC, Benzinger TLS, Raji CA. Sex-Specific Patterns of Body Mass Index Relationship with White Matter Connectivity. J Alzheimers Dis. 2022;86(4):1831-1848. doi: 10.3233/JAD-215329. PMID: 35180116; PMCID: PMC9108572.

This Special Issue will deal with pathophysiological as well as the phenomenological signs of insomnia. For example, Is ‘hyperarousal’ outdated? What is the difference between acute and chronic insomnia? What sleep disorders are linked to traumatic brain injuries? How is social facial recognition impacted by insomnia? What factors are linked to socioeconomic factors and alcohol consumption in the face of insomnia? We aim to set forward new thinking about insomnia research, including unchartered territories needing to be explored. Reviews, meta-analyses, and new experimental research are welcome in this Issue.