Big Data In Psychiatry And Neurology
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Big Data in Psychiatry and Neurology
Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer's disease and Parkinson's disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. - Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders - Analyzes methods in using big data to treat psychiatric and neurological disorders - Describes the role machine learning can play in the analysis of big data - Demonstrates the various methods of gathering big data in medicine - Reviews how to apply big data to genetics
Can Artificial Intelligence and Big Data Analytics Save the Future of Psychiatry?
This book is the second of the series about the imperatives for the search for new psychiatry. As stated in my recent 2021 book about: The Search for New Psychiatry, current psychiatric practices have failed many: patients and their families, their doctors and the society at large. That was the end of the 2021 book and the beginning of this book as a follow up in search for pathways to a new and more effective science-based practice Based on its major contributions to the recent successful and expedient development of the Covid 19 vaccines, I am proposing the same pathway of using the new revolution in informatics as the way to save and secure the future of psychiatry and that is what I am recommending in this book reaping the benefit of AI and Big Data Analytics but with a wide open eye on its limits, reliability, risks, unforeseen or unintentional harms. Part Two of the book deals with a number of perineal and also new challenges that continue to require better understanding and resolution. Among the phenomenological and nosological challenges, the recent development by Neurology of its subspeciality of Behavioral Neurology in competition to Neuropsychiatry, is reviewed in terms of an opportunity for integration of the tow subspecialities towards the creation of a new third field of “Clinical Neurosciences”. Other challenges included are: The Subjective /Objective Dichotomy, Lunacy and the Moon- reflections on the interactions of the brain and environment and Woke Psychiatry, what is it? Several other clinical challenges include: The Past is Coming Back as The Future -The Rise, Fall and Rise Again of Psychedelics, Loneliness as the silent disorder and several other challenges. At the end, a postscript has been hastily added in memory of a close friend, a pioneering psychopharmacologist but above all an empathic humanist, Professor Thomas Arthur Ban or as he always preferred, Tom.
The Ethics of Biomedical Big Data
This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. ‘Biomedical Big Data’ refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understanding of the ethical conundrums posed by biomedical Big Data, and shows how practitioners and policy-makers can address these issues going forward.