Free PDF Healthcare Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
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Healthcare Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Free PDF Healthcare Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
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Review
"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. Evaluation strategies for these processes are also discussed. My favorite part of the book is chapter 15 privacy-preserving data publishing methods in healthcare."―Ulrich Mansmann, Biometrics, December 2017
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About the Author
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."
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Product details
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series (Book 36)
Hardcover: 760 pages
Publisher: Chapman and Hall/CRC; 1 edition (June 23, 2015)
Language: English
ISBN-10: 1482232111
ISBN-13: 978-1482232110
Product Dimensions:
7.2 x 1.8 x 10.2 inches
Shipping Weight: 3.2 pounds (View shipping rates and policies)
Average Customer Review:
4.6 out of 5 stars
3 customer reviews
Amazon Best Sellers Rank:
#336,676 in Books (See Top 100 in Books)
Great material for my course.
This is an excellent text but the title is slightly misleading. The foreword advertises the book as something that can help bridge the gap between statistically trained and medically trained professionals who are working in or use health care data analytics. I cannot say the book completely fills that gap but it is still a useful text.What the book really is is a qualitative survey of all the ways modern data collection can influence healthcare, from sensors to the internet. Each of the chapters is written by experts in a particular discipline who summarize the current state of knowledge and provide citations for further research.What the book does not contain is any math or, for that matter, analytics. There is a brief chapter summarizing clinical prediction models but the math covered is the same as would be covered in chapter one of any graduate statistics text.Once you understand the intent of the book I could see it being used as a textbook in a graduate level healthcare data analytics course but it would have to be supplemented with a text that covers the various analytic techniques used by health care data analysts.As for self-study if you are interested in learning what is the state of the literature on a wide variety of health care data topics and their applications then the book would be worth purchasing. I can't believe there are many people, however, who have such an interest.In short, recommended but to a select group of readers.
Excellent book for Healthcare Analytics course. This covers different aspects in analytics depend on healthcare players.Provide details on EMR data formats and their classification. Also for a new person, advance methods for predictions and visualization are discussed in detail.
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