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Author: Ahmed Moustafa
Publisher: Academic Press
ISBN: 0128230029
Category : Medical
Languages : en
Pages : 386
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Book Description
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
Author: Ahmed Moustafa
Publisher: Academic Press
ISBN: 0128230029
Category : Medical
Languages : en
Pages : 386
Get Book
Book Description
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
Author: Ives Cavalcante Passos
Publisher: Springer
ISBN: 3030035530
Category : Medical
Languages : en
Pages : 180
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Book Description
This book integrates the concepts of big data analytics into mental health practice and research. Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century. Big data analytics is gaining traction in psychiatric research, being used to provide 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. Personalized Psychiatry – Big Data Analytics in Mental Health provides a unique opportunity to showcase innovative solutions tackling complex problems in mental health using big data and machine learning. It represents an interesting platform to work with key opinion leaders to document current achievements, introduce new concepts as well as project the future role of big data and machine learning in mental health.
Author: A. George Awad
Publisher: iUniverse
ISBN: 1663252688
Category : Biography & Autobiography
Languages : en
Pages : 193
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Book Description
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.
Author: Theodore A. Henderson
Publisher: Frontiers Media SA
ISBN: 2889766551
Category : Medical
Languages : en
Pages : 185
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Book Description
Author: Brent Daniel Mittelstadt
Publisher: Springer
ISBN: 3319335251
Category : Philosophy
Languages : en
Pages : 480
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Book Description
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.
Author: Houneida Sakly
Publisher: Springer Nature
ISBN: 3031111990
Category : Medical
Languages : en
Pages : 256
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Book Description
This book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others. This will include topics such as Artificial Intelligence of Thing (AIOT), Explainable Artificial Intelligence (XAI), Distributed learning, Blockchain of Internet of Things (BIOT), Cybersecurity, and Internet of (Medical) Things (IoTs). Healthcare providers will learn how to leverage Big Data analytics and AI as methodology for accurate analysis based on their clinical data repositories and clinical decision support. The capacity to recognize patterns and transform large amounts of data into usable information for precision medicine assists healthcare professionals in achieving these objectives. Intelligent Health has the potential to monitor patients at risk with underlying conditions and track their progress during therapy. Some of the greatest challenges in using these technologies are based on legal and ethical concerns of using medical data and adequately representing and servicing disparate patient populations. One major potential benefit of this technology is to make health systems more sustainable and standardized. Privacy and data security, establishing protocols, appropriate governance, and improving technologies will be among the crucial priorities for Digital Transformation in Healthcare.
Author: Daniel Barron
Publisher:
ISBN: 9781734420784
Category :
Languages : en
Pages :
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Book Description
Author: Rubin Wang
Publisher: Frontiers Media SA
ISBN: 2832540945
Category : Medical
Languages : en
Pages : 134
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Book Description
Author: Vincent Morelli
Publisher: Elsevier Health Sciences
ISBN: 0323661319
Category : Medical
Languages : en
Pages : 274
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Book Description
In this comprehensive look at adolescent screening and holistic health in the technology age, Dr. Vincent Morelli reviews the history of the adolescent health screen, what is being used now, and what needs to be considered in the future. An ideal resource for primary care physicians, pediatricians, and others in health care who work with adolescents, it consolidates today’s available information on this timely topic into a single convenient resource. Covers the history of the adolescent medical history and the need for an update of the biopsychosocial model, which has not significantly changed since 1977. Discusses nutrition screening, sleep screening, exercise screening, adverse childhood experiences (ACEs) screening, educational screening, behavioral and emotional screening, and more. Presents the knowledge and experience of leading experts who have assembled the most up-to-date recommendations for adolescent health screening. Explores today’s knowledge of health screening and discusses future directions to ensure healthy habits in adolescents, including education and self-efficacy.
Author: Marek Kubicki
Publisher: Springer Nature
ISBN: 3030352064
Category : Medical
Languages : en
Pages : 432
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Book Description
This comprehensive book explains the importance of imaging techniques in exploring and understanding the role of brain abnormalities in schizophrenia. The findings obtained using individual imaging modalities and their biological interpretation are reviewed in detail, and updates are provided on methodology, testable hypotheses, limitations, and new directions for research. The coverage also includes important recent applications of neuroimaging to schizophrenia, for example in relation to non-pharmacological interventions, brain development, genetics, and prediction of treatment response and outcome. Written by world renowned experts in the field, the book will be invaluable to all who wish to learn about the newest and most important developments in neuroimaging research in schizophrenia, how these developments relate to the last 30 years of research, and how they can be leveraged to bring us closer to a cure for this devastating disorder. Neuroimaging in Schizophrenia will assist clinicians in navigating what is an extremely complex field and will be a source of insight and stimulation for researchers.
