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Deep Learning for Healthcare Decision Making
Health care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. The health information industry is ready for big leaps and bounds in terms of growth and advancement. This book is an attempt to unveil the hidden potential of the enormous amount of health information and technology. Throughout this book, we attempt to combine numerous compelling views, guidelines, and frameworks to enable personalized health care service options through the successful application of deep learning frameworks. The progress of the health-care sector will be incremental as it learns from associations between data over time through the application of suitable AI, deep net frameworks, and patterns. The major challenge health care is facing is the effective and accurate learning of unstructured clinical data through the application of precise algorithms. Incorrect input data leading to erroneous outputs with false positives is intolerable in healthcare as patients’ lives are at stake. This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms. The specific focus of this book will be on the application of deep learning in any area of health care, including clinical trials, telemedicine, health records management, etc.
eBook
Big data in eHealthcare : challenges and perspectives
\"This book focuses on the different aspects of handling big data in healthcare. It showcases the current state-of-the-art technology used for storing health records and health data models. It also focuses on the research challenges in big data acquisition, storage, management and analysis\"-- Provided by publisher.
Book
Soft computing techniques in connected healthcare systems
\"This book provides examination of applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles that various techniques, such as machine learning, fuzzy logic, and statistical mathematics, play in the advancements of smart healthcare systems. This book is intended for under graduate, and graduate students, researchers and practicing professionals in the field of connected healthcare. It provides an overview for beginners while also addressing professionals in the industry on the importance of soft computing approaches in connected healthcare systems\"-- Provided by publisher.
Book
Progress Achieved, Landmarks, and Future Concerns in Biomedical and Health Informatics
Background: The biomedical and health informatics (BMHI) fields have been advancing rapidly, a trend particularly emphasised during the recent COVID-19 pandemic, introducing innovations in BMHI. Over nearly 50 years since its establishment as a scientific discipline, BMHI has encountered several challenges, such as mishaps, delays, failures, and moments of enthusiastic expectations and notable successes. This paper focuses on reviewing the progress made in the BMHI discipline, evaluating key milestones, and discussing future challenges. Methods: To, Structured, step-by-step qualitative methodology was developed and applied, centred on gathering expert opinions and analysing trends from the literature to provide a comprehensive assessment. Experts and pioneers in the BMHI field were assigned thematic tasks based on the research question, providing critical inputs for the thematic analysis. This led to the identification of five key dimensions used to present the findings in the paper: informatics in biomedicine and healthcare, health data in Informatics, nurses in informatics, education and accreditation in health informatics, and ethical, legal, social, and security issues. Results: Each dimension is examined through recently emerging innovations, linking them directly to the future of healthcare, like the role of artificial intelligence, innovative digital health tools, the expansion of telemedicine, and the use of mobile health apps and wearable devices. The new approach of BMHI covers newly introduced clinical needs and approaches like patient-centric, remote monitoring, and precision medicine clinical approaches. Conclusions: These insights offer clear recommendations for improving education and developing experts to advance future innovations. Notably, this narrative review presents a body of knowledge essential for a deep understanding of the BMHI field from a human-centric perspective and, as such, could serve as a reference point for prospective analysis and innovation development.
Journal Article
Mining the Biomedical Literature
The introduction of high-throughput methods has transformed biology into a data-rich science. Knowledge about biological entities and processes has traditionally been acquired by thousands of scientists through decades of experimentation and analysis. The current abundance of biomedical data is accompanied by the creation and quick dissemination of new information. Much of this information and knowledge, however, is represented only in text form--in the biomedical literature, lab notebooks, Web pages, and other sources. Researchers' need to find relevant information in the vast amounts of text has created a surge of interest in automated text-analysis. In this book, Hagit Shatkay and Mark Craven offer a concise and accessible introduction to key ideas in biomedical text mining. The chapters cover such topics as the relevant sources of biomedical text; text-analysis methods in natural language processing; the tasks of information extraction, information retrieval, and text categorization; and methods for empirically assessing text-mining systems. Finally, the authors describe several applications that recognize entities in text and link them to other entities and data resources, support the curation of structured databases, and make use of text to enable further prediction and discovery.
eBook
Digital health transformation with blockchain and artificial intelligence
\"This book covers the global digital revolution in the field of healthcare sector. The book encompasses chapters belong to the blockchain, Artificial Intelligence, and Big health data technologies\"-- Provided by publisher.
Book
Global health risks : mortality and burden of disease attributable to selected major risks
This report uses a comprehensive framework for studying health risks that was developed for the World Health Report 2002, which presented estimates for the year 2000. The report provides an update for the year 2004 for 24 global risk factors. It uses updated information from WHO programs and scientific studies for both exposure data and the causal associations of risk exposure to disease and injury outcomes. The burden of disease attributable to risk factors is measured in terms of lost years of healthy life using the metric of the disability-adjusted life year (DALY). The DALY combines years of life lost due to premature death with years of healthy life lost due to illness and disability. Health risks are in transition: populations are ageing owing to successes against infectious diseases; at the same time, patterns of physical activity and food, alcohol and tobacco consumption are changing. Low- and middle-income countries now face a double burden of increasing chronic, noncommunicable conditions, as well as the communicable diseases that traditionally affect the poor.
eBook