This book is the first to develop explicit methods for evaluating evidence of mechanisms in the field of medicine. It explains why it can be important to make this evidence explicit, and describes how to take such evidence into account in the evidence appraisal process. In addition, it develops procedures for seeking evidence of mechanisms, for evaluating evidence of mechanisms, and for combining this evaluation with evidence of association in order to yield an overall assessment of effectiveness. Evidence-based medicine seeks to achieve improved health outcomes by making evidence explicit and by developing explicit methods for evaluating it. To date, evidence-based medicine has largely focused on evidence of association produced by clinical studies. As such, it has tended to overlook evidence of pathophysiological mechanisms and evidence of the mechanisms of action of interventions. The book offers a useful guide for all those whose work involves evaluating evidence in the health sciences, including those who need to determine the effectiveness of health interventions and those who need to ascertain the effects of environmental exposures.

This open access book provides a set of conceptual, empirical, and comparative chapters that apply a public policy perspective to investigate the political and institutional factors driving the use of evidence to inform health policy in low, middle, and high income settings. The work presents key findings from the Getting Research Into Policy (GRIP-Health) project: a five year, six country, programme of work supported by the European Research Council. The chapters further our understanding of evidence utilisation in health policymaking through the application of theories and methods from the policy sciences. They present new insights into the roles and importance of factors such as issue contestation, institutional arrangements, logics of appropriateness, and donor influence to explore individual cases and comparative experiences in the use of evidence to inform health policy. Justin Parkhurst is Associate Professor at the London School of Economics and Political Science (the LSE)’s Department of Health Policy, UK. He has conducted research on a range of global health policy issues and on the politics of evidence. He served as the Principal Investigator of the GRIP-Health programme of work. Benjamin Hawkins is Associate Professor at the Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK. His research focuses on the role of research evidence and corporate actors in health policy making. In addition, he works on European integration, multi-level governance international trade and political economy approaches to health policy. Stefanie Ettelt is Associate Professor at the Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, UK. Her work examines the tensions between structure and agency in explaining the influence of evidence and research on policy-making and health system governance, particularly from a comparative perspective.

This open access book presents the roles and mechanisms of signal transduction triggered by nicotinic acetylcholine receptors (nAChRs) stimulation in neuroprotection against toxic effects of risk factors of neurodegenerative diseases. Accumulating evidence suggests that nAChRs in the CNS play important roles not only in excitatory neurotransmission but also in neuronal survival and related functions. Neuroprotection mediated by nAChRs in neurodegenerative diseases such as Alzheimer's disease is the major topic of this book. In response to rapidly evolving areas in clinical and laboratory neuropharmacology and neurochemistry, this volume provides in-depth coverage of neuroprotection in basic research and future developments in the clinical application of effective neuroprotective strategies in neurodegenerative diseases. This work appeals to both basic and clinical researchers in several fields, such as neuroscience, neurology, and pharmacology.

This book highlights the ethical issues and dilemmas that arise in the practice of public health. It is also a tool to support instruction, debate, and dialogue regarding public health ethics. Although the practice of public health has always included consideration of ethical issues, the field of public health ethics as a discipline is a relatively new and emerging area. There are few practical training resources for public health practitioners, especially resources which include discussion of realistic cases which are likely to arise in the practice of public health. This work discusses these issues on a case to case basis and helps create awareness and understanding of the ethics of public health care. The main audience for the casebook is public health practitioners, including front-line workers, field epidemiology trainers and trainees, managers, planners, and decision makers who have an interest in learning about how to integrate ethical analysis into their day to day public health practice. The casebook is also useful to schools of public health and public health students as well as to academic ethicists who can use the book to teach public health ethics and distinguish it from clinical and research ethics.

This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.