Academic Press 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. Review A comprehensive, up-to-date overview on the field of big data in psychiatry and neurology From the Back Cover Mental and neurological disorders represent a majority of the current public health challenge. According to the National Alliance of Mental Illness, 1 in 5 U.S. adults experience mental illness each year, however only 43.3% of adults are treated for their mental illness. It is imperative that mental disorders are effectively diagnosed and treated. In order to address this gap, analysis in science, industry, and government seek to utilize big data to affect clinical outcomes and diagnoses. 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. Big Data in Psychiatry and Neurology provides an up-to-date overview of the achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimers disease & Parkinsons disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students, and clinicians implement new methods to 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. About the Author Dr. Ahmed A. Moustafa is an associate professor in Cognitive and Behavioral Neuroscience at the Marcs Institute for Brain, Behavior, and Development, and in the School of Social Sciences and Psychology, Western Sydney University. He is trained in computer science, psychology, neuroscience, and cognitive science. His early training took place at Cairo University in mathematics and computer science. Before joining Western Sydney University as a lab director, he spent 11 years in America studying psychology and neuroscience. He researches computational and neuropsychological studies of addiction, schizophrenia, Parkinson’s disease, PTSD, and depression. He has published more than 150 papers in high-ranking journals including Science, PNAS, Journal of Neuroscience, Brain, Neuroscience and Biobehavioral Reviews, and Neuron.