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Leveraging Data Science for Global Health
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Leveraging Data Science for Global Health
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Global Health Informatics
Key concepts, frameworks, examples, and lessons learned in designing and implementing health information and communication technology systems in the developing world. The widespread usage of mobile phones that bring computational power and data to our fingertips has enabled new models for tracking and battling disease. The developing world in particular has become a proving ground for innovation in eHealth (using communication and technology tools in healthcare) and mHealth (using the affordances of mobile technology in eHealth systems). In this book, experts from a variety of disciplines—among them computer science, medicine, public health, policy, and business—discuss key concepts, fra...
Issues in Open Research Data
n 2010 the Panton Principles for Open Data in Science were published. These principles were founded upon the idea that Science is based on building on, reusing and openly criticising the published body of scientific knowledge’ (http://pantonprinciples.org) and they provide a succinct list of the fundamentals to observe when making your data open. Intended for a broad audience of academics, publishers and librarians, Issues in Research Data explores the implications of the Panton Principles through a number of perspectives on open research data in the sciences and beyond. The book features chapters by open data experts in a range of academic disciplines, covering practical information on licensing, ethics, and advice for data curators, alongside more theoretical issues surrounding the adoption of open data. As the book is open access, each chapter can stand alone from the main volume so that communities can host, distribute, build upon and remix the content that is relevant to them. Readers can access the online version via the QR code or DOI link at the front of the book.
AI in Clinical Medicine
AI IN CLINICAL MEDICINE An essential overview of the application of artificial intelligence in clinical medicine AI in Clinical Medicine: A Practical Guide for Healthcare Professionals is the definitive reference book for the emerging and exciting use of AI throughout clinical medicine. AI in Clinical Medicine: A Practical Guide for Healthcare Professionals is divided into four sections. Section 1 provides readers with the basic vocabulary that they require, a framework for AI, and highlights the importance of robust AI training for physicians. Section 2 reviews foundational ideas and concepts, including the history of AI. Section 3 explores how AI is applied to specific disciplines. Section...
Intensive Care Unit Telemedicine, An Issue of Critical Care Clinics
This issue of Critical Care Clinics, edited by Dr. Kianoush Kashani in collaboration with Consulting Editor Dr. John Kellum, is focused on Intensive Care Unit Telemedicine. Topics in this issue include: ICU telemedicine program administration: from start to full implementation; ICU telemedicine multidisciplinary care teams; ICU telemedicine technology; Impact of ICU telemedicine on outcomes; Quality assurance of ICU telemedicine; ICU telemedicine cost-effectiveness and financial analyses; ICU telemedicine care models; ICU telemedicine in the era of big data, artificial intelligence, and computer clinical decision support systems; ICU Telemedicine: Innovations and Limitations; ICU telemedicine: provider-patient satisfaction; and ICU telemedicine services beyond medical management: Tele-pharmacy, tele-procedure, tele-dialysis, tele-stroke: evidence, benefits, risks, and legal ramifications.
Artificial Intelligence for Medicine
Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications introduces readers to the methodology and AI/ML algorithms as well as cutting-edge applications to medicine, such as cancer, precision medicine, critical care, personalized medicine, telemedicine, drug discovery, molecular characterization, and patient mental health. Research in medicine and tailored clinical treatment are being quickly transformed by artificial intelligence (AI) and machine learning (ML). The content in this book is tailored to the reader's needs in terms of both type and fundamentals. It covers the current ethical issues and potential developments in this field. Artificial Intelligence ...
Applied Computer Vision and Soft Computing with Interpretable AI
This reference text presents the knowledge base of computer vision and soft computing techniques with their applications for sustainable developments. Features: ∙ Covers a variety of deep learning architectures useful for computer vision tasks. ∙ Demonstrates the use of different soft computing techniques and their applications for different computer vision tasks. ∙ Highlights the unified strengths of hybrid techniques based on deep learning and soft computing taken together that give the interpretable, adaptive, and optimized solution to a given problem. ∙ Addresses the different issues and further research opportunities in computer vision and soft computing. ∙ Describes all the concepts with practical examples and case studies with appropriate performance measures that validate the applicability of the respective technique to a certain domain. ∙ Considers recent real word problems and the prospective solutions to these problems. This book will be useful to researchers, students, faculty, and industry personnel who are eager to explore the power of deep learning and soft computing for different computer vision tasks.
Global Cardiac Surgery Capacity Development in Low and Middle Income Countries
This book provides a focused resource on how cardiac surgery capacity can be developed and how it assists in the sustainable development and strengthening of associated health systems. Background is provided on the extent of the problems that are experienced in many nations with suggestions for how suitable frameworks can be developed to improve cardiac healthcare provision. Relevant aspects of governance, financial modelling and disease surveillance are all covered. Guidance is also given on how to found and nurture cardiac surgery curriculum and residency programs. Global Cardiac Surgery Capacity Development in Low and Middle Income Countries provides a practically applicable resource on how to treat cardiac patients with limited resources. It identifies the key challenges and presents strategies on how these can be managed, therefore making it a critical tool for those involved in this field.
