Stemming from environmental, genetic, and situational factors, chronic disease is a critical concern in modern medicine. Managing treatment and controlling symptoms is imperative to the longevity and quality of life of patients with such diseases. The Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions features current research on the diagnosis, monitoring, management, and treatment of recurring diseases such as diabetes, Parkinson's disease, autoimmune disorders, and others. This handbook is intended for practitioners and researchers across various disciplines including, but not limited to, biology, biomedical engineering, computer science, and information and communication technologies. Aimed at identifying new disease determinants and the way in which new technologies can contribute to improved health outcomes, this handbook covers a variety of topics, including wearable and mobile technologies, capillaroscopy imaging, diagnostic and monitoring methods, and disease prediction modeling, among others.

Biomedical imaging enables physicians to evaluate areas of the body not normally visible, helping to diagnose and examine disease in patients. ""The Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications"" includes recent state-of-the-art methodologies that introduce biomedical imaging in decision support systems and their applications in clinical practice. This ""Handbook of Research"" provides readers with an overview of the emerging field of image-guided medical and biological decision support, bringing together various research studies and highlighting future trends. It includes: 30 authoritative contributions by over 90 of the world's leading experts on diagnostic imaging and biomedical applications from 9 countries; comprehensive coverage of each specific topic, highlighting recent trends and describing the latest advances in the field; and, more than 1,200 references to existing literature and research on diagnostic imaging and biomedical applications. A compendium of over 200 key terms with detailed definitions, this book is organized by topic and indexed, making it a convenient method of reference for all IT/IS scholars and professionals. It features cross-referencing of key terms, figures, and information pertinent to diagnostic imaging and biomedical applications.

Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii) regression technique (linear or non-linear), (iii) learning mechanism (batch or sequential), (iv) development and testing procedure and (v) scaling properties. In addition, simulation models of meal-derived glucose absorption and insulin dynamics and kinetics are covered, as an integral part of glucose predictive models. This book will help engineers and clinicians to: select a regression technique which can capture both linear and non-linear dynamics in glucose metabolism in diabetes, and which exhibits good generalization performance under stationary and non-stationary conditions; ensure the scalability of the optimization algorithm (learning mechanism) with respect to the size of the dataset, provided that multiple days of patient monitoring are needed to obtain a reliable predictive model; select a features set which efficiently represents both spatial and temporal dependencies between the input variables and the glucose concentration; select simulation models of subcutaneous insulin absorption and meal absorption; identify an appropriate validation procedure, and identify realistic performance measures.

Medical Data Sharing, Harmonization and Analytics serves as the basis for understanding the rapidly evolving field of medical data harmonization combined with the latest cloud infrastructures for storing the harmonized (shared) data. Chapters cover the latest research and applications on data sharing and protection in the medical domain, cohort integration through the recent advancements in data harmonization, cloud computing for storing and securing the patient data, and data analytics for effectively processing the harmonized data.

Atherosclerotic Plaque Characterization Methods Based on Coronary Imaging provides a complete review of computer methods for atherosclerotic plaque reconstruction and characterization. The authors, with their expertise from biomedical engineering, computer science, and cardiology, offer a holistic view. The focus of the book is on the presentation of major imaging techniques, including their limitations. It includes details on the mechanical characterization and properties of plaques and appropriate constitutive models to describe the mechanical behavior of plaques. The authors explore the challenges of using multiple coronary imaging technologies, and provide the pros and cons of invasive vs. non-invasive techniques. Methods for plaque characterization and 3D reconstruction of coronary arteries using IVUS, OCT, and CT images are described. This book will help readers study new trends in image processing analysis and plaque characterization, implement automated plaque characterization methodologies, understand coronary imaging drawbacks, and comprehend 3 dimensional coronary artery and plaque reconstruction methods.

Multiscale Modelling in Biomedical Engineering Discover how multiscale modeling can enhance patient treatment and outcomes In Multiscale Modelling in Biomedical Engineering, an accomplished team of biomedical professionals delivers a robust treatment of the foundation and background of a general computational methodology for multi-scale modeling. The authors demonstrate how this methodology can be applied to various fields of biomedicine, with a particular focus on orthopedics and cardiovascular medicine. The book begins with a description of the relationship between multiscale modeling and systems biology before moving on to proceed systematically upwards in hierarchical levels from the molecular to the cellular, tissue, and organ level. It then examines multiscale modeling applications in specific functional areas, like mechanotransduction, musculoskeletal, and cardiovascular systems. Multiscale Modelling in Biomedical Engineering offers readers experiments and exercises to illustrate and implement the concepts contained within. Readers will also benefit from the inclusion of: A thorough introduction to systems biology and multi-scale modeling, including a survey of various multi-scale methods and approaches and analyses of their application in systems biology Comprehensive explorations of biomedical imaging and nanoscale modeling at the molecular, cell, tissue, and organ levels Practical discussions of the mechanotransduction perspective, including recent progress and likely future challenges In-depth examinations of risk prediction in patients using big data analytics and data mining Perfect for undergraduate and graduate students of bioengineering, biomechanics, biomedical engineering, and medicine, Multiscale Modelling in Biomedical Engineering will also earn a place in the libraries of industry professional and researchers seeking a one-stop reference to the basic engineering principles of biological systems.

This book addresses issues of scattering theory and biomedical engineering, as well as methodological approaches and tools from related scientific areas such as applied mathematics, mechanics, numerical analysis, and signal and image processing.