How to Read a Paper: The Basics of Evidence-based Medicine and Healthcare (7版)

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【簡介】 重量：0.51kg 頁數： 328 裝訂：平裝　開數：21.6 x 14 cm 印刷:雙色 Learn to assess published research in this best-selling introduction to evidence-based healthcare Evidence-based practices have revolutionized medical care. Clinical and scientific papers have something to offer practitioners at every level of the profession, from students to established clinicians in medicine, nursing and allied professions. Novices are often intimidated by the idea of reading and appraising the research literature. How to Read a Paper demystifies this process with a thorough, engaging introduction to how clinical research papers are constructed and how to evaluate them. Now fully updated to incorporate new areas of research, readers of the seventh edition of How to Read a Paper will also find: A careful balance between the principles of evidence-based healthcare and clinical practice New chapters covering consensus methods, mechanistic evidence, big data and artificial intelligence Detailed coverage of subjects like assessing methodological quality, systemic reviews and meta-analyses, qualitative research, and more. How to Read a Paper is ideal for all healthcare students and professionals seeking an accessible introduction to evidence-based healthcare – particularly those sitting undergraduate and postgraduate exams and preparing for interviews.

Behavioral Emergencies for Healthcare Providers (2版)

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Chapter “Depression in the Emergency Department” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. This fully updated second edition focuses on mental illness, both globally and in terms of specific mental-health-related visits encountered in emergency department settings, and provides practical input from physicians experienced with adult emergency psychiatric patients. It covers the pre-hospital setting and advising on evidence-based practice; from collaborating with psychiatric colleagues to establishing a psychiatric service in your emergency department. Potential dilemmas when treating pregnant, geriatric or homeless patients with mental illness are discussed in detail, along with the more challenging behavioral diagnoses such as substance abuse, factitious and personality disorders, delirium, dementia, and PTSD. The new edition of Behavioral Emergencies for Healthcare Providers will be an invaluable resource for psychiatrists, psychologists, psychiatric and emergency department nurses, trainee and experienced emergency physicians, and other mental health workers.

Artificial Intelligence and Healthcare (1版)

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This book explores the ethical problems of algorithmic bias and its potential impact on populations that experience health disparities by examining the historical underpinnings of explicit and implicit bias, the influence of the social determinants of health, and the inclusion of racial and ethnic minorities in data. Over the last twenty-five years, the diagnosis and treatment of disease have advanced at breakneck speeds. Currently, we have technologies that have revolutionized the practice of medicine, such as telemedicine, precision medicine, big data, and AI. These technologies, especially AI, promise to improve the quality of patient care, lower health care costs, improve patient treatment outcomes, and decrease patient mortality. AI may also be a tool that reduces health disparities; however, algorithmic bias may impede its success. This book explores the risks of using AI in the context of health disparities. It is of interest to health services researchers,ethicists, policy analysts, social scientists, health disparities researchers, and AI policy makers.

Healthcare Industry 4.0: Computer Vision-Aided Data Analytics (Computational Intelligence Techniques) 2024

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This book covers computer vision-based applications in digital healthcare industry 4.0, including different computer vision techniques, image classification, image segmentations, and object detection. Various application case studies from domains such as science, engineering, and social networking are introduced, along with their architecture and how they leverage various technologies, such as edge computing and cloud computing. It also covers applications of computer vision in tumor detection, cancer detection, combating COVID-19, and patient monitoring. Features: Provides a state-of-the-art computer vision application in the digital health care industry Reviews advances in computer vision and data science technologies for analyzing information on human function and disability Includes practical implementation of computer vision application using recent tools and software Explores computer vision-enabled medical/clinical data security in the cloud Includes case studies from the leading computer vision integrated vendors like Amazon, Microsoft, IBM, and Google This book is aimed at researchers and graduate students in bioengineering, intelligent systems, and computer science and engineering.