In Memory of Ethics: A Dissection of Ethical and Social Issues in Pakistani Professional Healthcare Practice explores the ethical challenges faced within the healthcare sector with a focus on Pakistan. From workplace behaviors to medical procedures, COVID-19 era dilemmas, and broader social health concerns, the book explores the intricate intersection of ethics and healthcare in the region. The book is divided into five sections. Section I covers topics such as workplace behaviors and ethical conducts while section II covers medical procedures, and patient-doctor relationships. Section III addresses the unique ethical challenges brought forth by the COVID-19 pandemic, including guidelines for therapists and the ethics of mandatory vaccinations. Section IV examines the impact of social media on healthcare, gender discrimination, and the ethical considerations surrounding prisoner rehabilitation. The final section offers insightful essays on ethical and social Issues, covering topics like body shaming, free healthcare, and patient privacy. The editors also explore controversial topics, including euthanasia, abortion, and assisted reproductive technology. The key feature of this book is that it provides a comprehensive coverage of diverse ethical dilemmas in healthcare. Furthermore, it is an attempt to give a subjective understanding of healthcare ethics through thought-provoking essays shedding light on contemporary ethical issues in a Pakistani context. The contributors have incorporated real-world examples and case studies to illustrate ethical principles in action. In Memory of Ethics: A Dissection of Ethical and Social Issues in Pakistani Professional Healthcare Practice book is essential reading for anyone interested in understanding complex ethical and social issues and promoting integrity in healthcare practices.

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.