eBooks
Alzheimer's Disease: Understanding Biomarkers, Big Data, and Therapy by
Nearly 44 million people have Alzheimer's or related dementia worldwide, according to the Alzheimer's Disease International organization. That number is expected to double every 20 years. Unlike other books on the market, Alzheimer's Disease: Understanding Biomarkers, Big Data, and Therapy covers recent advancements in cognitive, clinical, neural, and therapeutic aspects of Alzheimer's and other forms of dementia. First, readers are introduced to cognitive and clinical studies, focusing on the different types of memory impairment, past and future thinking. This includes the prevalence of depression, its relationship to other symptoms, and the quality of life for those with Alzheimer's disease. In addition, the book discusses recent studies on memory dysfunction in advanced-stage Alzheimer's disease, in comparison to early-stage, including a chapter on the underlying factors in the transition from mild cognitive impairment to Alzheimer's diagnosis. Following this section, the book presents recent studies on the role of different cortical and subcortical structures in the development of various symptoms in Alzheimer's disease, as well as different neural biomarkers underlying the development and treatment of the disease. In the last section of the book, therapeutic aspects of Alzheimer's disease, focusing on behavioral and pharmacological treatments of sleep disorders, memory problems, and depression, are reviewed. The book aids readers in understanding the advances in research and care, making it a prime tool for all clinicians, psychologists, researchers, neurologists, and caregivers of dementia patients.
ISBN: 9780128213346Publication Date: 2021-09-15
Artificial Intelligence, Machine Learning, and Mental Health in Pandemics by
Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach provides a comprehensive guide for public health authorities, researchers and health professionals in psychological health. The book takes a unique approach by exploring how Artificial Intelligence (AI) and Machine Learning (ML) based solutions can assist with monitoring, detection and intervention for mental health at an early stage. Chapters include computational approaches, computational models, machine learning based anxiety and depression detection and artificial intelligence detection of mental health. With the increase in number of natural disasters and the ongoing pandemic, people are experiencing uncertainty, leading to fear, anxiety and depression, hence this is a timely resource on the latest updates in the field.
ISBN: 9780323911962Publication Date: 2022-04-22
Big Data in Psychiatry and Neurology by
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.
