Healthcare Data Analytics
1st Edition
Published on 23. June 2015
Book
Hardback
760 pages
978-1-4822-3211-0 (ISBN)
Description
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.
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.
Reviews / Votes
"Anyone with experience in data analytics who is coming into the field of healthcare should make time to read this book ..."-Computing Reviews
"... an outstanding book that contains a resourceful introduction to fundamental knowledge in data sources and basic analysis, as well as a presentation of updated research with respect to data analytic methods and applications in healthcare practice. The book balances the various levels of detail to meet the needs of researchers and practitioners with diverse backgrounds and interests. ... a highly recommended book for those who wish to explore the healthcare data analytics domain."
-Journal of Biomedical Informatics, 58, 2015
"The volume Healthcare Data Analytics by Reddy and Aggarwal is more technical and gives a comprehensive introduction to fundamental principles, algorithms, and applications of health data acquisition, processing, and analysis. It starts with a survey on electronic health records (EHR), a central instrument for collecting heath data and putting hese data into context. The next chapters present biomedical image data, sensor data, genomic data, and the processing of clinical text by natural language processing (NLP). Further relevant sources of health data are the biomedical literature and social media. Chapter 10 is on clinical prediction models and offers the classical biostatistical toolbox. Over the next three chapters, more complex models based on longitudinal, spatial, and high-dimensional data are discussed. The presentation uses the machine-learning perspective but offers many references from the biostatistical literature. Chapter 14 discusses information retrieval for healthcare. Its overall goal is to find content which meets information needs. The interplay of two processes determines the success of information retrieval: Indexing assigns metadata to content items, retrieval produces content items based on the user's query. Evaluat
More details
Series
Language
English
Place of publication
Boca Raton
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Professional and scholarly
Professional Practice & Development
Illustrations
193 s/w Abbildungen, 46 s/w Tabellen
46 Tables, black and white; 193 Illustrations, black and white
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 45 mm
Weight
1602 gr
ISBN-13
978-1-4822-3211-0 (9781482232110)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Chandan K. Reddy | Charu C. Aggarwal
Healthcare Data Analytics
Book
06/2020
1st Edition
Chapman & Hall/CRC
€71.00
Shipment within 15-20 days
Chandan K. Reddy | Charu C. Aggarwal
Healthcare Data Analytics
E-Book
06/2015
Chapman & Hall/CRC
€65.99
Available for download
Chandan K. Reddy | Charu C. Aggarwal
Healthcare Data Analytics
E-Book
06/2015
1st Edition
Chapman and Hall
€65.99
Available for download
Persons
Chandan K. Reddy is an associate professor in the Department of Computer Science at Wayne State University. He received his PhD from Cornell University and MS from Michigan State University. His primary research interests are in the areas of data mining and machine learning with applications to healthcare, bioinformatics, and social network analysis. His research is funded by the National Science Foundation, the National Institutes of Health, Department of Transportation, and the Susan G. Komen for the Cure Foundation. He has published over 50 peer-reviewed articles in leading conferences and journals. He received the Best Application Paper Award at the ACM SIGKDD conference in 2010 and was a finalist of the INFORMS Franz Edelman Award Competition in 2011. He is a senior member of IEEE and a life member of ACM.
Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his BS from IIT Kanpur in 1993 and his PhD from the Massachusetts Institute of Technology in 1996. He has published more than 250 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is an author or editor of 13 books, including the first comprehensive book on outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bioterrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, a recipient of the IBM Outstanding Technical Achievement Award (2009) for his work on data streams, and a recipient of an IBM Research Division Award (2008) for his contributions to System S. He also received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving data mining. He has served as conference chair and associate editor at many reputed conferences and journals in data mining, general co-chair of the IEEE Big Data Conference (2014), and is editor-in-chief of the ACM SIGKDD Explorations. He is a fellow of the ACM and the IEEE, for "contributions to knowledge discovery and data mining algorithms."
Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his BS from IIT Kanpur in 1993 and his PhD from the Massachusetts Institute of Technology in 1996. He has published more than 250 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is an author or editor of 13 books, including the first comprehensive book on outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bioterrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, a recipient of the IBM Outstanding Technical Achievement Award (2009) for his work on data streams, and a recipient of an IBM Research Division Award (2008) for his contributions to System S. He also received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving data mining. He has served as conference chair and associate editor at many reputed conferences and journals in data mining, general co-chair of the IEEE Big Data Conference (2014), and is editor-in-chief of the ACM SIGKDD Explorations. He is a fellow of the ACM and the IEEE, for "contributions to knowledge discovery and data mining algorithms."
Content
Healthcare Data Sources and Basic Analytics. Advanced Data Analytics For Healthcare. Applications and Practical Systems for Healthcare .