Artificial Intelligence, Machine Learning, and Mental Health in Pandemics


Book Description

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. Examines the datasets and algorithms that can be used to detect mental disorders Covers machine learning solutions that can help determine the precautionary measures of psychological health problems Highlights innovative AI solutions and bi-statistics computation that can strengthen day-to-day medical procedures and decision-making




Researches and Applications of Artificial Intelligence to Mitigate Pandemics


Book Description

Researches and Applications of Artificial Intelligence to Mitigate Pandemics: History, Diagnostic Tools, Epidemiology, Healthcare, and Technology offers readers an interdisciplinary view of state-of-art research related to the COVID-19 outbreak, with a focus on tactics employed to model the number of cases of COVID-19 (time series modeling), models employed to diagnostics COVID-19 based on images, and the panoramic of COVID-19 since its discovery and up to this book's publication. This book showcases the algorithms and models available to manage pandemic data, the role of AI, IoT and Mathematical Modeling, how to prevent and fight COVID-19, and the existing medical, social and pharmaceutical support. Chapters cover methods and protocols, the basics and history of diseases, the fast diagnosis of disease with different automated algorithms and artificial intelligence tools and techniques, the methods of handling epidemiology for mitigating the spread of disease, artificial intelligence and mathematical modeling techniques, and how mental and physical health is affected with social media usage. Explains novel and hybrid high quality artificial intelligence methodologies, techniques, algorithms, architectures, tools and methods to cope with pandemics Covers rapid point-of-care diagnostics, presents details on varied mathematical models developed to control epidemiology, and lists existing measures to disseminate the spread of infection using computational methods Highlights the negative effect of social media and other sources by applying preventive measures to combat depression and anxiety




Applications of Artificial Intelligence in COVID-19


Book Description

The book examines the role of artificial intelligence during the COVID-19 pandemic, including its application in i) early warnings and alerts, ii) tracking and prediction, iii) data dashboards, iv) diagnosis and prognosis, v) treatments, and cures, and vi) social control. It explores the use of artificial intelligence in the context of population screening and assessing infection risks, and presents mathematical models for epidemic prediction of COVID-19. Furthermore, the book discusses artificial intelligence-mediated diagnosis, and how machine learning can help in the development of drugs to treat the disease. Lastly, it analyzes various artificial intelligence-based models to improve the critical care of COVID-19 patients.




Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease


Book Description

Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.




Artificial Intelligence in Healthcare and COVID-19


Book Description

Artificial Intelligence in Healthcare and COVID-19 showcases theoretical concepts and implementational and research perspectives surrounding AI. The book addresses both medical and technological visions, making it even more applied. With the advent of COVID-19, it is obvious that leading universities and medical schools must include these topics and case studies in their usual courses of health informatics to keep up with the pace of technological and medical advancements. This book will also serve professors teaching courses and industry practitioners and professionals working in the R&D team of leading medical and informatics companies who want to embrace AI and eHealth to fight COVID-19. Since AI in healthcare is a comparatively new field, there exists a vacuum of literature in this field, especially when applied to COVID-19. With the area of AI in COVID-19 being quite young, students and researchers usually face a struggle to rely on the few published papers (which are obviously too specific) or whitepapers by tech-giants (which are too wide). Discusses the fundamentals and theoretical concepts of applying AI in healthcare pertaining to COVID-19 Provides a landscape view to the applied aspect of AI in healthcare related COVID-19 through case studies and innovative applications Discusses key concerns and challenges in the field of AI in eHealth during the pandemic, along with other allied fields like IoT, creating a broad platform of transdisciplinary discussion




Artificial Intelligence in Behavioral and Mental Health Care


Book Description

Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. Summarizes AI advances for use in mental health practice Includes advances in AI based decision-making and consultation Describes AI applications for assessment and treatment Details AI advances in robots for clinical settings Provides empirical data on clinical efficacy Explores practical issues of use in clinical settings




System Design for Epidemics Using Machine Learning and Deep Learning


Book Description

This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.




Leveraging Artificial Intelligence in Global Epidemics


Book Description

Leveraging Artificial Intelligence in Global Epidemics provides readers with a detailed technical description of the role Artificial Intelligence plays in various stages of a disease outbreak, using COVID-19 as a case study. In the fight against epidemics, medical staff are on the front line; but behind the lines the battle is fought by researchers, and data scientists. Artificial Intelligence has been helping researchers with computer modeling and simulation for predictions about disease progression, the overall economic situation, tax incomes and population development. In the same manner, AI can prepare researchers for any emergency situation by backing the medical science. Artificial Intelligence plays a key and cutting-edge role in the preparedness for and dealing with the outbreak of global epidemics. It can help researchers analyze global data about known viruses to predict the patterns of the next pandemic and the impacts it will have. Not only prediction, AI plays an increasingly important role in assessing readiness, early detection, identification of patients, generating recommendations, situation awareness and more. It is up to the right input and the innovative ways by humans to leverage what AI can do. As COVID-19 has grabbed the world and its economy today, an analysis of the COVID-19 outbreak and the global responses and analytics will pay a long way in preparing humanity for such future situations. Provides readers with understanding of how Artificial Intelligence can be applied to the prediction, forecasting, detection, and testing of global epidemics, using COVID-19 and other recent epidemics such as Ebola, Corona viruses, Zika, influenza, Dengue, Chikungaya, and malaria as case studies Includes background material regarding readiness for coping with epidemics, including Machine Learning models for prediction of epidemic outbreaks based on existing data Includes technical coverage of key topics such as generating recommendations to combat outbreaks, genome sequencing, AI-assisted testing, AI-assisted contact tracing, situation awareness and combating disinformation, and the role of Artificial Intelligence and Machine Learning in drug discovery, vaccine development, and drug re-purposing




Enabling Healthcare 4.0 for Pandemics


Book Description

ENABLING HEALTHCARE 4.0 for PANDEMICS The book explores the role and scope of AI, machine learning and other current technologies to handle pandemics. In this timely book, the editors explore the current state of practice in Healthcare 4.0 and provide a roadmap for harnessing artificial intelligence, machine learning, and Internet of Things, as well as other modern cognitive technologies, to aid in dealing with the various aspects of an emergency pandemic outbreak. There is a need to improvise healthcare systems with the intervention of modern computing and data management platforms to increase the reliability of human processes and life expectancy. There is an urgent need to come up with smart IoT-based systems which can aid in the detection, prevention and cure of these pandemics with more precision. There are a lot of challenges to overcome but this book proposes a new approach to organize the technological warfare for tackling future pandemics. In this book, the reader will find: State-of-the-art technological advancements in pandemic management; AI and ML-based identification and forecasting of pandemic spread; Smart IoT-based ecosystem for pandemic scenario. Audience The book will be used by researchers and practitioners in computer science, artificial intelligence, bioinformatics, data scientists, biomedical statisticians, as well as industry professionals in disaster and pandemic management.




Artificial Intelligence and Mental Health Care


Book Description

New developments in machine learning (ML) and artificial intelligence (AI) hold great promise to revolutionize mental health care. In this context, ML and AI have been deployed for several different goals, including 1) the early detection of mental disorders, 2) the optimization of personalized treatments based on the individual characteristics of patients, 3) the better characterization of disorders detrimental to mental well-being and quality of life, as well as a better description of projected trajectories over time, and 4) the development of new treatments for mental health care. Despite their great potential to transform mental health care and occasional breakthroughs, ML and AI have not yet fully achieved these goals. This research topic aims to bridge the gap between the potential uses of ML and AI and their practical application in standard mental health care. More specifically, we welcome original research submissions applying ML and AI to promote public health by reducing the burden of chronic disorders with detrimental effects on well-being (e.g., psychopathological distress), and improving quality of life. We also welcome submissions applying ML and AI in heterogeneous datasets (e.g., subjective scales and questionnaires, biomarkers, (neuro)psychological assessments, etc.) from Big Data sources (e.g., large datasets of clinical populations, electronic health records from nationally representative cohorts, and/or biobanks, studies using experiencing sampling methods, etc.) to gain mechanistic insight on how different chronic conditions associated with psychopathological distress can affect patient well-being and quality of life. Finally, we also welcome opinion papers and reviews on how to develop AI applications in mental health care responsibly, while integrating biopsychosocial aspects of patients to promote better mental health care.