Medical modelling and the principles of medical imaging, Computer Aided Design (CAD) and Rapid Prototyping (also known as Additive Manufacturing and 3D Printing) are important techniques relating to various disciplines - from biomaterials engineering to surgery. Building on the success of the first edition, Medical Modelling: The application of Advanced Design and Rapid Prototyping techniques in medicine provides readers with a revised edition of the original text, along with key information on innovative imaging techniques, Rapid Prototyping technologies and case studies.Following an overview of medical imaging for Rapid Prototyping, the book goes on to discuss working with medical scan data and techniques for Rapid Prototyping. In this second edition there is an extensive section of peer-reviewed case studies, describing the practical applications of advanced design technologies in surgical, prosthetic, orthotic, dental and research applications. - Covers the steps towards rapid prototyping, from conception (modelling) to manufacture (manufacture) - Includes a comprehensive case studies section on the practical application of computer-aided design (CAD) and rapid prototyping (RP) - Provides an insight into medical imaging for rapid prototyping and working with medical scan data

This unified modeling textbook for students of biomedical engineering provides a complete course text on the foundations, theory and practice of modeling and simulation in physiology and medicine. It is dedicated to the needs of biomedical engineering and clinical students, supported by applied BME applications and examples. Developed for biomedical engineering and related courses: speaks to BME students at a level and in a language appropriate to their needs, with an interdisciplinary clinical/engineering approach, quantitative basis, and many applied examples to enhance learning Delivers a quantitative approach to modeling and also covers simulation: the perfect foundation text for studies across BME and medicine Extensive case studies and engineering applications from BME, plus end-of-chapter exercises

Modelling Methodology for Physiology and Medicine offers a unique approach and an unprecedented range of coverage of the state-of-the-art, advanced modelling methodology that is widely applicable to physiology and medicine. The book opens with a clear and integrated treatment of advanced methodology for developing mathematical models of physiology and medical systems. Readers are then shown how to apply this methodology beneficially to real-world problems in physiology and medicine, such as circulation and respiration. - Builds upon and enhances the readers existing knowledge of modelling methodology and practice - Editors are internationally renowned leaders in their respective fields

Mixture Modelling for Medical and Health Sciences provides a direct connection between theoretical developments in mixture modelling and their applications in real world problems. The book describes the development of the most important concepts through comprehensive analyses of real and practical examples taken from real-life research problems in

In financially constrained health systems across the world, increasing emphasis is being placed on the ability to demonstrate that health care interventions are not only effective, but also cost-effective. This book deals with decision modelling techniques that can be used to estimate the value for money of various interventions including medical devices, surgical procedures, diagnostic technologies, and pharmaceuticals. Particular emphasis is placed on the importance of the appropriate representation of uncertainty in the evaluative process and the implication this uncertainty has for decision making and the need for future research. This highly practical guide takes the reader through the key principles and approaches of modelling techniques. It begins with the basics of constructing different forms of the model, the population of the model with input parameter estimates, analysis of the results, and progression to the holistic view of models as a valuable tool for informing future research exercises. Case studies and exercises are supported with online templates and solutions. This book will help analysts understand the contribution of decision-analytic modelling to the evaluation of health care programmes. ABOUT THE SERIES: Economic evaluation of health interventions is a growing specialist field, and this series of practical handbooks will tackle, in-depth, topics superficially addressed in more general health economics books. Each volume will include illustrative material, case histories and worked examples to encourage the reader to apply the methods discussed, with supporting material provided online. This series is aimed at health economists in academia, the pharmaceutical industry and the health sector, those on advanced health economics courses, and health researchers in associated fields.

Many published books that comment on the medical model have been written by doctors, who assume that readers have the same knowledge of medicine, or by those who have attempted to discredit and attack the medical practice. Both types of book have tended to present diagnostic categories in medicine as universally scientifically valid examples of clear-cut diseases easily distinguished from each other and from health; with a fixed prognosis; and with a well-understood aetiology leading to disease-reversing treatments. These are contrasted with psychiatric diagnoses and treatments, which are described as unclear and inadequate in comparison. The Medical Model in Mental Health: An Explanation and Evaluation explores the overlap between the usefulness of diagnostic constructs (which enable prognosis and treatment decisions) and the therapeutic effectiveness of psychiatry compared with general medicine. The book explains the medical model and how it applies in mental health, assuming little knowledge or experience of medicine, and defends psychiatry as a medical practice.

The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow 'with' and 'without' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. - Provides a step-by-step approach to develop deep learning models - Presents case studies showing end-to-end implementation (source codes: available upon request)

Medical Modelling:?The Application of Advanced Design and Additive Manufacturing Techniques in Medicine, Third Edition?provides readers with a thorough update of the core contents, along with key information on innovative imaging techniques, additive manufacturing technologies and a range of applied case studies. This comprehensive new edition includes new coverage of advanced technologies, such as selective laser melting, electron beam melting, multi jet fusion, and more. The extensive section of peer-reviewed case studies is thoroughly updated and includes additional clinical examples, describing the practical applications of advanced design technologies in surgical, prosthetic, orthotic, dental and research applications. Finally, Medical Modelling:?The Application of Advanced Design and Additive Manufacturing Techniques in Medicine, Third Edition explores the future potential of medical modelling, such as in simulations for training, the development of new medical devices and so on. - Covers the essential stages and methods of creating virtual and physical anatomical models from medical scan data - Presents an overview of the main AM processes, including advantages and limitations - Provides worked examples and case studies with detailed descriptions of the applications of 3D scanning, CAD, and AM to a wide variety of anatomical, surgical, prosthetic, orthotic, and associated applications

This book contributes to better understand how lifestyle modulations can effectively halt the emergence and progression of human diseases. The book will allow the reader to gain a better understanding of the mechanisms by which the environment interferes with the bio-molecular regulatory processes underlying the emergence and progression of complex diseases, such as cancer. Focusing on key and early cellular bio-molecular events giving rise to the emergence of degenerative chronic disease, it builds on previous experience on the development of multi-cellular organisms, to propose a mathematical and computer based framework that allows the reader to analyze the complex interplay between bio-molecular processes and the (micro)-environment from an integrative, mechanistic, quantitative and dynamical perspective. Taking the wealth of empirical evidence that exists it will show how to build and analyze models of core regulatory networks involved in the emergence and progression of chronic degenerative diseases, using a bottom-up approach.