This book offers in-depth reviews of different techniques and novel approaches of using blockchain and artificial intelligence in smart healthcare services. The volume brings 14 reviews and research articles written by academicians, researchers and industry professionals to give readers a current perspective of smart healthcare solutions for medical and public health services. The book starts with examples of how blockchain can be applied in healthcare services such as the care of osteoporosis patients and security. Several chapters review AI models for disease detection including breast cancer, colon cancer and anemia. The authors have included model design and parameters for the benefit of professionals who want to implement specific algorithms. Furthermore, the book also includes chapters on IoT frameworks for smart healthcare systems, giving readers a primer on how to utilize the technology in this sector. Additional use cases for machine learning for gesture learning. COVID-19 management, and sentiment analysis.

Trauma-informed care is designed to assist persons who have experienced adversity and focuses on change at the clinical and organizational level. Its goals center around prevention, intervention, and treatments that are evidence-based, encourage resilience, and enhance coping. This textbook is designed to give a comprehensive overview of trauma-informed care to students and faculty involved in nursing care programs. Key features: · Explains the skill sets to assess and care for persons who have experienced trauma. · Emphasizes key principles of trauma-informed care · Includes the use of client-centered, person-centered, and resilience-based tools to deal with trauma · Recommends trauma recovery from a positive psychology and post-traumatic growth perspective · Utilizes a caring pedagogy intended to foster resilience and help offset the secondary traumatic stress and compassion fatigue experienced by student and practicing nurses. · Communicates the value of fostering psychological safety, compassion satisfaction, and joy in work · Includes narrative case studies and learning activities in all chapters to help the reader to actively engage with the subject matter. · Presents self-care strategies to enhance physical and emotional well-being.

Prediction in Medicine: The Impact of Machine Learning on Healthcare explores the transformative power of advanced data analytics and machine learning in healthcare. This comprehensive guide covers predictive analysis, leveraging electronic health records (EHRs) and wearable devices to optimize patient care and healthcare planning. Key topics include disease diagnosis, risk assessment, and precision medicine advancements in cardiovascular health and hypertension management. The book also addresses challenges in interpreting clinical data and navigating ethical considerations. It examines the role of AI in healthcare emergencies and infectious disease management, highlighting the integration of diverse data sources like medical imaging and genomic data. Prediction in Medicine is essential for students, researchers, healthcare professionals, and general readers interested in the future of healthcare and technological innovation.

This reference demonstrates the development of a context aware decision-making health informatics system with the objective to automate the analysis of human centric wellness and assist medical decision-making in healthcare. The book introduces readers to the basics of a clinical decision support system. This is followed by chapters that explain how to analyze healthcare data for anomaly detection and clinical correlations. The next two sections cover machine learning techniques for object detection and a case study for hemorrhage detection. These sections aim to expand the understanding of simple and advanced neural networks in health informatics. The authors also explore how machine learning model choices based on context can assist medical professionals in different scenarios. Key Features : -Reader-friendly format with clear headings, introductions and summaries in each chapter -Detailed references for readers who want to conduct further research -Expert contributors providing authoritative knowledge on machine learning techniques and human-centric wellness -Practical applications of data science in healthcare designed to solve problems and enhance patient wellbeing -Deep learning use cases for different medical conditions including hemorrhages, gallbladder stones and diabetic retinopathy Demonstrations of fast and efficient CNN models with varying parameters such as Single shot detector, R-CNN, Mask R-CNN, modified contrast enhancement and improved LSTM models. This reference is intended as a primary resource for professionals, researchers, software developers and technicians working in healthcare informatics systems and medical diagnostics. It also serves as a supplementary resource for learners in bioinformatics, biomedical engineering and medical informatics programs and anyone who requires technical knowledge about algorithms in medical decision support systems.

With an increase of the population of elderly people in modern society due to advances in medicine and healthcare facilities, there is also an increase in the incidence and duration of chronic illnesses. Similarly, advanced age is considered a significant determinant of depression and poor quality of life. Additionally, the provision of therapies relevant to chronic diseases addresses the issues beyond the concept of cure, bringing to the center the need for a dignified quality of life of patients. An increased interest in quality of life is observed in patients who suffer from chronic diseases, including those with end-stage kidney disease (ESKD). End-stage kidney disease patients have a high burden of disease affecting their quality of life and dramatically shortening their life expectancy. Therefore, exploring quality of life issues among such patients becomes an essential task in the management of this population. This volume is written by researchers with a well-rounded understanding of the characteristics and impact of ESKD and provides a penetrating practical discussion to date of alternative approaches for comprehensively measuring the burden of end-stage kidney disease. Outcomes Assessment in End-Stage Kidney Disease focuses on the impact of this complex and exacting condition on patients and those that care for them. It also examines the economic impact of ESKD and the healthcare policy implications of this disease. It also brings comprehensive and thoughtful insights into the issues confronted by patients with ESKD to readers interested in nursing or medical caregiving, geriatrics and health psychology.