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At the intersection of computer science and healthcare, data
analytics has emerged as a promising tool for solving problems
across many healthcare-related disciplines. Supplying a
comprehensive overview of recent healthcare analytics research,
Healthcare Data Analytics provides a clear understanding of the
analytical techniques currently available to solve healthcare
problems. The book details novel techniques for acquiring,
handling, retrieving, and making best use of healthcare data. It
analyzes recent developments in healthcare computing and discusses
emerging technologies that can help improve the health and
well-being of patients. Written by prominent researchers and
experts working in the healthcare domain, the book sheds light on
many of the computational challenges in the field of medical
informatics. Each chapter in the book is structured as a
"survey-style" article discussing the prominent research issues and
the advances made on that research topic. The book is divided into
three major categories: Healthcare Data Sources and Basic Analytics
- details the various healthcare data sources and analytical
techniques used in the processing and analysis of such data
Advanced Data Analytics for Healthcare - covers advanced analytical
methods, including clinical prediction models, temporal pattern
mining methods, and visual analytics Applications and Practical
Systems for Healthcare - covers the applications of data analytics
to pervasive healthcare, fraud detection, and drug discovery along
with systems for medical imaging and decision support Computer
scientists are usually not trained in domain-specific medical
concepts, whereas medical practitioners and researchers have
limited exposure to the data analytics area. The contents of this
book will help to bring together these diverse communities by
carefully and comprehensively discussing the most relevant
contributions from each domain.
Research on the problem of clustering tends to be fragmented across
the pattern recognition, database, data mining, and machine
learning communities. Addressing this problem in a unified way,
Data Clustering: Algorithms and Applications provides complete
coverage of the entire area of clustering, from basic methods to
more refined and complex data clustering approaches. It pays
special attention to recent issues in graphs, social networks, and
other domains. The book focuses on three primary aspects of data
clustering: Methods, describing key techniques commonly used for
clustering, such as feature selection, agglomerative clustering,
partitional clustering, density-based clustering, probabilistic
clustering, grid-based clustering, spectral clustering, and
nonnegative matrix factorization Domains, covering methods used for
different domains of data, such as categorical data, text data,
multimedia data, graph data, biological data, stream data,
uncertain data, time series clustering, high-dimensional
clustering, and big data Variations and Insights, discussing
important variations of the clustering process, such as
semisupervised clustering, interactive clustering, multiview
clustering, cluster ensembles, and cluster validation In this book,
top researchers from around the world explore the characteristics
of clustering problems in a variety of application areas. They also
explain how to glean detailed insight from the clustering
process-including how to verify the quality of the underlying
clusters-through supervision, human intervention, or the automated
generation of alternative clusters.
At the intersection of computer science and healthcare, data
analytics has emerged as a promising tool for solving problems
across many healthcare-related disciplines. Supplying a
comprehensive overview of recent healthcare analytics research,
Healthcare Data Analytics provides a clear understanding of the
analytical techniques currently available to solve healthcare
problems. The book details novel techniques for acquiring,
handling, retrieving, and making best use of healthcare data. It
analyzes recent developments in healthcare computing and discusses
emerging technologies that can help improve the health and
well-being of patients. Written by prominent researchers and
experts working in the healthcare domain, the book sheds light on
many of the computational challenges in the field of medical
informatics. Each chapter in the book is structured as a
"survey-style" article discussing the prominent research issues and
the advances made on that research topic. The book is divided into
three major categories: Healthcare Data Sources and Basic Analytics
- details the various healthcare data sources and analytical
techniques used in the processing and analysis of such data
Advanced Data Analytics for Healthcare - covers advanced analytical
methods, including clinical prediction models, temporal pattern
mining methods, and visual analytics Applications and Practical
Systems for Healthcare - covers the applications of data analytics
to pervasive healthcare, fraud detection, and drug discovery along
with systems for medical imaging and decision support Computer
scientists are usually not trained in domain-specific medical
concepts, whereas medical practitioners and researchers have
limited exposure to the data analytics area. The contents of this
book will help to bring together these diverse communities by
carefully and comprehensively discussing the most relevant
contributions from each domain.
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