تحسين النتائح
ترتيب حسب
من
الى
الصفحة 1
الصفحة 1
Cancer drug safety and public health policy : A changing landscape
Description and analysis of recent developments and current trends in health policy with regard to cancer drug safety. Opens with an overview of pharmacovigilance for cancer blockbuster drugs, covering both general considerations and efforts to develop a structured framework for the identification and reporting of adverse drug reactions (ADRs). A number of important examples of serious ADRs to hematology and oncology drugs are then reviewed, with evaluation of the lessons learned and the policy implications of the ensuing legal cases and their settlements. Further, the difficulty of reporting such blockbuster side effects in the medical literature is explored in an empirical study. Significant advances have been achieved in analytic methods for the identification of ADRs, and here there is a particular focus on the value of optimal discriminant analysis. Finally, the impacts on pharmacovigilance and drug safety of the huge fines paid under the U.S. False Claims Act relating to the defrauding of governmental programs also receive careful attention – these fines are playing an important role in changing the landscape for pharmaceutical safety.
Machine learning in healthcare : Fundamentals and recent applications
Discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises.
Machine learning and deep learning in medical data analytics and healthcare applications
Introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.
Anti-Microbial Resistance in Global Perspective
This book provides an accessible introduction to the mechanics of international development and global health text for policy-makers and students across a wide range of disciplines.
