Sudden Cardiac Death (SCD) is a sudden, unexpected death caused by loss of heart function (sudden cardiac arrest) and Sudden Cardiac Arrest (SCA) occurs when the electrical system to the heart malfunctions and suddenly becomes very irregular. Death can often be a result if not handled quick enough or effectively. New technologies seek to help with this issue. Data processing is a crucial step to developing prognostic models. Some of the challenges in data processing are non-linear prediction models, a large number of patients and numerous predictors with complicated correlations. In traditional hypothesis-driven statistical analysis it is difficult to overcome these challenges. Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. So, there is an emergent need of an adaptation of AI technologies such as Machine Learning and Deep Learning Techniques to overcome the challenges. The Machine Learning (ML) approaches have great potential in increasing the accuracy of cardiovascular risk prediction and to avoid unnecessary treatment. The application of ML techniques may have the potential to improve Heart Failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Moreover, ML algorithms can also be applied to predict SCD. Also, Machine Learning offers an opportunity to improve accuracy by exploiting complex interactions between risk factors. The book addresses the impact and power of technology driven approaches for prevention and detection of SCA and SCD. It will provide insights on causes and symptoms of SCA and SCD and evaluate whether AI Technologies can improve the accuracy of cardiovascular risk prediction. It will explore the current issues and future technology driven solutions for SCA and SCD prevention and detection.

In recent years, there have been significant progress in computational intelligence and image processing with machine learning and deep learning as important components of modern artificial intelligence. All these progresses face challenges in dealing with Covid-19 pandemic for detection and treatment.This comprehensive compendium provides not only updated advances of computational intelligence and image processing in the detection and treatment of Covid-19, but also other medical applications such as in cancer detection and cardiovascular diseases, etc. More traditional approaches such as 2D segmentation and 3D reconstruction are included.The useful reference text is an updated version of the edited title, Computer Vision in Medical Imaging (World Scientific, 2014) and its companion volume, Frontiers of Medical Imaging (World Scientific, 2015). The book is written for engineers, scientists and the medical community to meet the increased challenges in medical applications.

The Pacific Symposium on Biocomputing (PSB) 2022 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2022 will be held on January 3 - 7, 2022 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2022 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.

The Internet serves as an essential tool in promoting health awareness through the circulation of important research among the medical professional community. While digital tools and technologies have greatly improved healthcare, challenges are still prevalent among diverse populations worldwide. The Handbook of Research on Advancing Health Education through Technology presents a comprehensive discussion of health knowledge equity and the importance of the digital age in providing life-saving data for diagnosis and treatment of diverse populations with limited resources. Featuring timely, research-based chapters across a broad spectrum of topic areas including, but not limited to, online health information resources, data management and analysis, and knowledge accessibility, this publication is an essential reference source for researchers, academicians, medical professionals, and upper level students interested in the advancement and dissemination of medical knowledge.

This book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.

The internet of medical things provides significant advantages for the well-being of society by increasing the quality of life and reducing medical expenses. An important step towards a smart healthcare system is to utilize the potential of existing technologies in order to deliver the best services to users and improve their circumstances. With the help of internet of medical things technologies, self-care and early diagnosis are influential services in strengthening the healthcare ecosystem, especially those which utilize remote monitoring systems. The Internet of Medical Things (IoMT) and Telemedicine Frameworks and Applications focuses on the role of artificial intelligence, the internet of medical things, and telemedicine as well as the advantages and challenges that can occur from the integration of these technologies. The book also evolves methodologies to develop frameworks for the integration of the internet of medical things and telemedicine. Covering topics such as remote healthcare, medical imaging, and data science, this reference work is ideal for researchers, academicians, scholars, practitioners, instructors, and students.