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This doctoral thesis reports on an innovative data repository offering adaptive metadata management to maximise information sharing and comprehension in multidisciplinary and geographically distributed collaborations. It approaches metadata as a fluid, loosely structured and dynamical process rather than a fixed product, and describes the development of a novel data management platform based on a schemaless JSON data model, which represents the first fully JSON-based metadata repository designed for the biomedical sciences. Results obtained in various application scenarios (e.g. integrated biobanking, functional genomics and computational neuroscience) and corresponding performance tests are reported on in detail. Last but not least, the book offers a systematic overview of data platforms commonly used in the biomedical sciences, together with a fresh perspective on the role of and tools for data sharing and heterogeneous data integration in contemporary biomedical research.
Over recent years there has been major investment in research infrastructure to harness the potential of routinely collected health data. In 2013, The Farr Institute for Health Informatics Research was established in the UK, undertaking health informatics research to enhance patient and public health by the analysis of data from multiple sources and unleashing the value of vast sources of clinical, biological, population and environmental data for public benefit. The Medical Informatics Europe (MIE) conference is already established as a key event in the calendar of the European Federation of Medical Informatics (EFMI); The Farr Institute has been establishing a conference series. For 2017, the decision was made to combine the power and established reputational excellence of EFMI with the emerging and innovative research of The Farr Institute community to create ‘Informatics for Health 2017’, a joint conference that creates a scientific forum allowing these two communities to share knowledge, insights and experience, advance cross-disciplinary thinking, and stimulate creativity. This book presents the 116 full papers presented at that conference, held in Manchester, UK in April 2017. The papers are grouped under five headings: connected and digital health; health data science; human, organisational, and social aspects; knowledge management; and quality, safety, and patient outcomes, and the book will be of interest to all those whose work involves the analysis and use of data to support more effective delivery of healthcare.
Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of biomedical informatics, medical imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of medical imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies.
Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems. The very successful synergistic combination of methodologies and approaches from Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions for the vision to support human intelligence with machine learning. The papers selected for this volume focus on hot topics in smart health; they discuss open problems and future challenges in order to provide a research agenda to stimulate further research and progress.
Biobanks represent an invaluable research tool and, as a result of their intrinsic and extrinsic nature, may be looked upon as archives or repositories largely made up of libraries, or collections of content where the content is the biological material derived from different individuals or species, representing valuable tangible assets. Biobanks analyses aspects of the commons and common intellectual property relating to the concepts of private property, not only concerning data but biological materials as well, and the advantages and disadvantages of patents in scientific research. Several recent initiatives in biomedical research have attempted to make their data freely available to others, so as to foster innovation. Many of these initiatives have adopted the open source model, which has gained widespread recognition in the computer industry. This title is structured into eight chapters and begins with an introduction, which is followed by chapters that discuss how the term 'biobank' came about in scientific literature; legal matters relating to biobanks; and intellectual and physical property. Later chapters comprehensively analyse the intellectual property of biobanks within the sphere of copyright; biotechnological inventions and research patentability; open data sharing in biobanks; and biobanks as commons or vault. - Considers biobanks as both repositories and as collections of tangible assets - Argues that the data in biobanks represents a high value intangible asset - Explores regulatory gaps exploited by the private sector
Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. - Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions - Provides background, milestone and examples of precision medicine - Outlines the paradigm shift towards precision medicine driven by value-based systems - Discusses future applications of precision medicine research using AI - Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Brain Banking, Volume 150, serves as the only book on the market offering comprehensive coverage of the functional realities of brain banking. It focuses on brain donor recruitment strategies, brain bank networks, ethical issues, brain dissection/tissue processing/tissue dissemination, neuropathological diagnosis, brain donor data, and techniques in brain tissue analysis. In accordance with massive initiatives, such as BRAIN and the EU Human Brain Project, abnormalities and potential therapeutic targets of neurological and psychiatric disorders need to be validated in human brain tissue, thus requiring substantial numbers of well characterized human brains of high tissue quality with neurological and psychiatric diseases. - Offers comprehensive coverage of the functional realities of brain banking, with a focus on brain donor recruitment strategies, brain bank networks, ethical issues, and more - Serves as a valuable resource for staff in existing brain banks by highlighting best practices - Enhances the sharing of expertise between existing banks and highlights a range of techniques applicable to banked tissue for neuroscience researchers - Authored by leaders from brain banks around the globe – the broadest, most expert coverage available
When is it appropriate to return individual research results to participants? The immense interest in this question has been fostered by the growing movement toward greater transparency and participant engagement in the research enterprise. Yet, the risks of returning individual research resultsâ€"such as results with unknown validityâ€"and the associated burdens on the research enterprise are competing considerations. Returning Individual Research Results to Participants reviews the current evidence on the benefits, harms, and costs of returning individual research results, while also considering the ethical, social, operational, and regulatory aspects of the practice. This report includes 12 recommendations directed to various stakeholdersâ€"investigators, sponsors, research institutions, institutional review boards (IRBs), regulators, and participantsâ€"and are designed to help (1) support decision making regarding the return of results on a study-by-study basis, (2) promote high-quality individual research results, (3) foster participant understanding of individual research results, and (4) revise and harmonize current regulations.
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.