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This Research Topic is Volume II of a series. The previous volume can be found here: Advances and Applications of Artificial Intelligence and Numerical Simulation in Risk Emergency Management and Treatment Our world is composed of multidimensional and multifaceted risks. In general, geological, environmental, and ecological risks would exist in both natural and engineering situations, such as karst desertification, water inrush, rock burst, debris flow, and landslide. These risks have great safety threats to human survival. In this regard, risk emergency management and treatment (REMT) has become a pivotal topic addressing the national governance system and its governance capacity. It underlines how to prevent and resolve grand security risks, to timely respond to all kinds of disasters and accidents, as well as to safeguard people’s lives and property and social stability.
In a world where the relentless force of natural and man-made disasters threatens societies, the need for effective disaster management has never been more critical. Predicting Natural Disasters With AI and Machine Learning addresses the challenges of disasters and charts a path toward proactive solutions by applying artificial intelligence (AI) and machine learning (ML). This book begins by interpreting the nature of disasters, clearly distinguishing between natural and man-made hazards. It delves into the intricacies of disaster risk reduction (DRR), emphasizing the human contribution to most disasters. Recognizing the necessity for a multifaceted approach, the book advocates the four ‘R’s - Risk Mitigation, Response Readiness, Response Execution, and Recovery - as integral components of comprehensive disaster management. This book explores various AI and ML applications designed to predict, manage, and mitigate the impact of natural disasters, focusing on natural language processing, and early warning systems. The contrast between weak AI, simulating human intelligence for specific tasks, and strong AI, capable of autonomous problem-solving, is thoroughly examined in the context of disaster management. Its chapters systematically address critical issues, including real-world data handling, challenges related to data accessibility, completeness, security, privacy, and ethical considerations.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
Artificial intelligence (AI) has shown promise as an effective tool in disaster preparedness and response, providing a unique perspective on some of the most urgent health challenges. Rapid advances in AI technology can potentially revolutionize the way how we respond to emergencies and disasters that affect the world's health, including early warning systems, resource allocation, and real-time decision-making. This Research Topic aims to explore the latest developments in AI and its applications in global health and disaster response, providing a comprehensive overview of the potential and challenges of AI in improving health outcomes in crises. This Research Topic will bring together leading researchers, practitioners, and policymakers in global health and disaster response to share their experiences and insights on how AI can be leveraged to improve response efforts and enhance healthcare delivery.
This book provides the most current and comprehensive overview available today of the critical role of information systems in emergency response and preparedness. It includes contributions from leading scholars, practitioners, and industry researchers, and covers all phases of disaster management - mitigation, preparedness, response, and recovery. 'Foundational' chapters provide a design framework and review ethical issues. 'Context' chapters describe the characteristics of individuals and organizations in which EMIS are designed and studied. 'Case Study' chapters include systems for distributed microbiology laboratory diagnostics to detect possible epidemics or bioterrorism, humanitarian MIS, and response coordination systems. 'Systems Design and Technology' chapters cover simulation, geocollaborative systems, global disaster impact analysis, and environmental risk analysis. Throughout the book, the editors and contributors give special emphasis to the importance of assessing the practical usefulness of new information systems for supporting emergency preparedness and response, rather than drawing conclusions from a theoretical understanding of the potential benefits of new technologies.
The rapid advancements in artificial intelligence technology have paved the way for the use of machine learning applications in health care. These applications address existing challenges in the emergency department such as triage and disposition, early detection of conditions and outcomes, emergency department operations, and therapeutic interventions. Artificial intelligence can be used in three ways in the context of emergency and critical care. The first one is to build risk stratification prediction models in critical care. The second use of AI involves utilizing unsupervised machine learning techniques to divide the varied population into homogeneous subgroups. The third use of AI is for reinforcement learning algorithms to prescribe treatment regimens in a sequential way. The dynamic treatment regime (DTR) model uses reinforcement learning to estimate a set of decision rules, one for each step of intervention. It specifies how to tailor treatments to patients considering their treatment and covariate histories. DTR lowers model complexity and is considered more appropriate for medical epidemiology. This book is a vital tool for all researching or studying the role of AI in emergency medicine. It aims to equip students and experts with the advanced topics and upcoming concepts in this subject.
This Research Topic is the fourth volume of the series Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine Volume I: Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume I Volume II:Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume II Volume III:Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume III Analytics based on artificial intelligence has greatly advanced scientific research fields like natural language processing and imaging classification. Clinical research has also greatly benefited from artificial intelligence. Emergency and critical care physicians face patients with rapidly changing conditions, which require accurate risk stratification and initiation of rescue therapy. Furthermore, critically ill patients, such as those with sepsis, acute respiratory distress syndrome, and trauma, are comprised of heterogeneous population. The “one-size-fit-all” paradigm may not fit for the management of such heterogeneous patient population. Thus, artificial intelligence can be employed to identify novel subphenotypes of these patients. These sub classifications can provide not only prognostic value for risk stratification but also predictive value for individualized treatment. With the development of transcriptome providing a large amount of information for an individual, artificial intelligence can greatly help to identify useful information from high dimensional data. Altogether, it is of great importance to further utilize artificial intelligence in the management of critically ill patients.
While medical specialists in disaster mitigation, preparedness, and response are needed worldwide, the initial phase of disaster response is almost entirely dependent upon local resources—making it essential that all healthcare personnel have a working knowledge of the field and stand ready to integrate into the response system. Ciottone's Disaster Medicine, 3rd Edition, is the most comprehensive reference available to help accomplish these goals in every community. It thoroughly covers isolated domestic events as well as global disasters and humanitarian crises. Dr. Gregory Ciottone and more than 200 worldwide authorities share their knowledge and expertise on the preparation, assessment, and management of both natural and man-made disasters, including lessons learned by the responders to contemporary disasters such as the COVID-19 pandemic, Australian and western U.S. wildfires, European heatwaves, the Beirut explosion, recent hurricanes and typhoons, and the global refugee crisis. Part 1 offers an A-to-Z resource for every aspect of disaster medicine and management, while Part 2 features an exhaustive compilation of every conceivable disaster event, organized to facilitate quick reference in a real-time setting. Covers basic concepts such as identification of risks, organizational preparedness, equipment planning, disaster education and training, and more advanced concepts such as disaster risk reduction, health in complex emergencies, building local disaster resiliency, psychological impact of disasters on children, and more. Contains new decision trees throughout that help guide you through the decision-making process in difficult situations. Uses an easy-to-follow, templated approach to historical perspectives, overviews of current practice including pre-incident and post-incident actions, medical treatment of casualties, and potential pitfalls. Includes updated sections on man-made disasters, including mass casualties, active shooter situations, integrated response to terrorist attacks, and chemical/biological/radiological/nuclear/high-yield explosives disasters. Discusses the latest technologies, such as the use of mobile disaster applications, drone response systems, and virtual reality simulation training. Features thoroughly updated information on crisis leadership, practical applications of disaster epidemiology, disaster and climate change, and the integration of non-government agencies (NGOs) in disaster response—a critical topic for those responding to humanitarian needs overseas. Includes new chapters on Pandemic Preparedness and Response, Disaster Medicine in a Changing Climate, Disaster Response in Asia, Building Local Capacity and Disaster Resiliency, Civilian-Military Coordination in Disaster Response, Medical Simulation in Disaster Preparedness, Disaster Nursing, Crisis Meta-Leadership, Palliative Care in Disasters, Counter-Terrorism Medicine, SARS CoV (COVID-19 and SARS), and Disasters in Space Travel.