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.

BEST OF THE 2022 RUSA Book & Media AWARDS One of Biblioracle's 8 favorite nonfiction books of 2021 in the Chicago Tribune The New York Post's BEST BOOKS OF 2021 USA Today's 5 BOOKS NOT TO MISS Alexander nimbly and grippingly translates the byzantine world of American health care into a real-life narrative with people you come to care about. --New York Times Takes readers into the world of the American medical industry in a way no book has done before. --Fortune By following the struggle for survival of one small-town hospital, and the patients who walk, or are carried, through its doors, The Hospital takes readers into the world of the American medical industry in a way no book has done before. Americans are dying sooner, and living in poorer health. Alexander argues that no plan will solve America's health crisis until the deeper causes of that crisis are addressed. Bryan, Ohio's hospital, is losing money, making it vulnerable to big health systems seeking domination and Phil Ennen, CEO, has been fighting to preserve its independence. Meanwhile, Bryan, a town of 8,500 people in Ohio's northwest corner, is still trying to recover from the Great Recession. As local leaders struggle to address the town's problems, and the hospital fights for its life amid a rapidly consolidating medical and hospital industry, a 39-year-old diabetic literally fights for his limbs, and a 55-year-old contractor lies dying in the emergency room. With these and other stories, Alexander strips away the wonkiness of policy to reveal Americans' struggle for health against a powerful system that's stacked against them, but yet so fragile it blows apart when the pandemic hits. Culminating with COVID-19, this book offers a blueprint for how we created the crisis we're in.

Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data discusses the insight of data processing applications in various domains through soft computing techniques and enormous advancements in the field. The book focuses on the cross-disciplinary mechanisms and ground-breaking research ideas on novel techniques and data processing approaches in handling structured and unstructured healthcare data. It also gives insight into various information-processing models and many memories associated with it while processing the information for forecasting future trends and decision making. This book is an excellent resource for researchers and professionals who work in the Healthcare Industry, Data Science, and Machine learning.

Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction. AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients.

The use of eHealth and mHealth interventions for health promotion, health professional education, and health professional support is on the rise. They have a significant potential for learning through their wide reach, ability to tailor to specific needs, and facilitation of engagement, interactivity, and collaboration. Although eHealth and mHealth interventions are invested in quality and effectiveness, they vary in their use of theory and instructional design principles based on the perspectives of the disciplines that can influence their work. Instructional Design Exemplars in eHealth and mHealth Education Interventions showcases design exemplars of eHealth and mHealth interventions in health promotion and in education and support of health professionals. These exemplars demonstrate the integration of theory and design principles that benefit health professionals and health education. Covering topics such as healthcare access, instructional technology, and diverse learning experiences, this book is a dynamic resource for health professionals, instructional designers, educators, researchers, hospital administrators, policymakers, researchers, and academicians.