R for Health Technology Assessment discusses the use of proper statistical software, specifically R, to perform the whole pipeline of analytic modelling in health technology assessment (HTA).
This fascinating collection examines the socio-economic factors that impact the well-being of patients with sickle cell disease (SCD) in Sub-Saharan Africa and the critical importance of patient advocacy in the region.
This important collection provides an epidemiological perspective on the continuing scope of sickle cell disease (SCD) in sub-Saharan Africa, alongside the clinical attempts to provide comprehensive care in a resource-limited setting.
In the early 1980s, it had only recently been appreciated that what was known of the epidemiology of dementia in the elderly living in the community was just the tip of a large iceberg.
In the early 1980s, it had only recently been appreciated that what was known of the epidemiology of dementia in the elderly living in the community was just the tip of a large iceberg.
This fascinating collection examines the socio-economic factors that impact the well-being of patients with sickle cell disease (SCD) in Sub-Saharan Africa and the critical importance of patient advocacy in the region.
This important collection provides an epidemiological perspective on the continuing scope of sickle cell disease (SCD) in sub-Saharan Africa, alongside the clinical attempts to provide comprehensive care in a resource-limited setting.
In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.
Designed for students training to become biostatisticians as well as practicing biostatisticians, Inference Principles for Biostatisticians presents the theoretical and conceptual foundations of biostatistics.
A book for men and women from middle age to advanced years who have suddenly and unexpectedly become confronted with the huge effects that the male prostate can have on the body.
Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background.
Statistical Genetics is an advanced textbook focusing on conducting genome-wide linkage and association analysis in order to identify the genes responsible for complex behaviors and diseases.
Association Models in Epidemiology: Study Designs, Modeling Strategies, and Analytic Methods is written by an epidemiologist for graduate students, researchers, and practitioners who will use regression techniques to analyze data.
The Applied Genomic Epidemiology Handbook: A Practical Guide to Leveraging Pathogen Genomic Data in Public Health provides rationale, theory, and implementation guidance to help public health practitioners incorporate pathogen genomic data analysis into their investigations.
Healthcare systems globally are grappling with how best to implement effective and efficient patient-centred care while simultaneously trying to contain runaway costs and provide high quality.
In recent decades, there has been enormous growth in biologics research and development, with the accompanying development of biological assays for emerging products.
The revolution in healthcare as well as demand for efficient real-time healthcare services are driving the progression of edge computing, AI-mediated techniques, deep learning, and IoT applications for healthcare industries and cloud computing.
Healthcare systems globally are grappling with how best to implement effective and efficient patient-centred care while simultaneously trying to contain runaway costs and provide high quality.
The Applied Genomic Epidemiology Handbook: A Practical Guide to Leveraging Pathogen Genomic Data in Public Health provides rationale, theory, and implementation guidance to help public health practitioners incorporate pathogen genomic data analysis into their investigations.
The revolution in healthcare as well as demand for efficient real-time healthcare services are driving the progression of edge computing, AI-mediated techniques, deep learning, and IoT applications for healthcare industries and cloud computing.
Artificial intelligence (AI) in medicine is rising, and it holds tremendous potential for more accurate findings and novel solutions to complicated medical issues.
This volume addresses SDG 3 from a mathematical standpoint, sharing novel perspectives of existing communicable disease modelling technologies of the next generation and disseminating new developments in modelling methodologies and simulation techniques.
This volume addresses SDG 3 from a mathematical standpoint, sharing novel perspectives of existing communicable disease modelling technologies of the next generation and disseminating new developments in modelling methodologies and simulation techniques.
"e;I recommend that all members of the health community read this book to obtain a real snapshot of how the Intelligent Health System is being transformed via new technologies.
"e;I recommend that all members of the health community read this book to obtain a real snapshot of how the Intelligent Health System is being transformed via new technologies.
This handbook provides thorough, in-depth, and well-focused developments of artificial intelligence (AI), machine learning (ML), deep learning (DL), natural language processing (NLP), cryptography, and blockchain approaches, along with their applications focused on healthcare systems.
This book addresses the origins, determinants and magnitude of the global problem of sedentary behaviour, along with concise yet in-depth solutions for tackling it.
Value of Information for Healthcare Decision-Making introduces the concept of Value of Information (VOI) use in health policy decision-making to determine the sensitivity of decisions to assumptions, and to prioritise and design future research.
Cancer screening has been carried out for six decades - however, there are many unsolved problems: how to estimate key parameters involved in screenings, such as sensitivity, the time duration in the preclinical state (i.
