Healthcare Data Analytics: 1st Edition (Hardback) book cover

Healthcare Data Analytics

1st Edition

Edited by Chandan K. Reddy, Charu C. Aggarwal

Chapman and Hall/CRC

760 pages | 193 B/W Illus.

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pub: 2015-06-23
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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.


"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. Evaluationstrategies for these processes are also discussed. My favoritepart of the book is chapter 15 privacy-preserving data publishing methods in healthcare."

—Ulrich Mansmann, Biometrics, December 2017

Table of Contents

An Introduction to Healthcare Data Analytics; Chandan K. Reddy and Charu C. Aggarwal


Healthcare Data Sources and Basic Analytics

Advanced Data Analytics for Healthcare

Applications and Practical Systems for Healthcare

Resources for Healthcare Data Analytics



Electronic Health Records: A Survey; Rajiur Rahman and Chandan K. Reddy


History of EHR

Components of HER

Coding Systems

Benefits of EHR

Barriers to Adopting EHR

Challenges of Using EHR Data

Phenotyping Algorithms


Biomedical Image Analysis; Dirk Padfield, Paulo Mendonca, and Sandeep Gupta


Biomedical Imaging Modalities

Object Detection

Image Segmentation

Image Registration

Feature Extraction

Conclusion and Future Work

Mining of Sensor Data in Healthcare: A Survey; Daby Sow, Kiran K. Turaga, Deepak S. Turaga, and Michael Schmidt


Mining Sensor Data in Medical Informatics: Scope and Challenges

Challenges in Healthcare Data Analysis

Sensor Data Mining Applications

Nonclinical Healthcare Applications

Summary and Concluding Remarks

Biomedical Signal Analysis; Abhijit Patil, Rajesh Langoju, Suresh Joel, Bhushan D. Patil, and Sahika Genc


Types of Biomedical Signals

ECG Signal Analysis.

Denoising of Signals

Multivariate Biomedical Signal Analysis

Cross-Correlation Analysis

Recent Trends in Biomedical Signal Analysis


Genomic Data Analysis for Personalized Medicine; Juan Cui


Genomic Data Generation

Methods and Standards for Genomic Data Analysis

Types of Computational Genomics Studies towards Personalized Medicine

Genetic and Genomic Studies to theBedside of Personalized Medicine

Concluding Remarks

Natural Language Processing and Data Mining for Clinical Text; Kalpana Raja and Siddhartha R. Jonnalagadda


Natural Language Processing

Mining Information from Clinical Text

Challenges of Processing Clinical Reports

Clinical Applications


Mining the Biomedical Literature; Claudiu Mihaila, Riza Batista-Navarro, Noha Alnazzawi, Georgios Kontonatsios, Ioannis Korkontzelos, Rafal Rak, Paul Thompson, and Sophia Ananiadou



Terminology Acquisition and Management


Discourse Interpretation

Text Mining Environments


Integration with Clinical Text Mining


Social Media Analytics for Healthcare; Alexander Kotov


Social Media Analysis for Detection and Tracking of Infectious Disease

Social Media Analysis for Public Health Research

Analysis of Social Media Use in Healthcare

Conclusions and Future Directions


A Review of Clinical Prediction Models; Chandan K. Reddy and Yan Li


Basic Statistical Prediction Models

Alternative Clinical Prediction Models

Survival Models

Evaluation and Validation


Temporal Data Mining for Healthcare Data; Iyad Batal


Association Analysis

Temporal Pattern Mining

Sensor Data Analysis

Other Temporal Modeling Methods



Visual Analytics for Healthcare; David Gotz, Jesus Caban, and Annie T. Chen


Introduction to Visual Analytics and Medical Data Visualization

Visual Analytics in Healthcare


Predictive Models for Integrating Clinical and Genomic Data; Sanjoy Dey, Rohit Gupta, Michael Steinbach, and Vipin Kumar


Issues and Challenges in Integrating Clinical and Genomic Data

Different Types of Integration

Different Goals of Integrative Studies


Discussion and Future Work

Information Retrieval for Healthcare; William R. Hersh


Knowledge-Based Information in Healthcare and Biomedicine

Content of Knowledge-Based Information Resources




Research Directions


Privacy-Preserving Data Publishing Methods in Healthcare; Yubin Park and Joydeep Ghosh


Data Overview and Preprocessing

Privacy-Preserving Publishing Methods

Challenges with Health Data



Data Analytics for Pervasive Health; Giovanni Acampora, Diane J. Cook, Parisa Rashidi, and Athanasios V. Vasilakos


Supporting Infrastructure and Technology

Basic Analytic Techniques

Advanced Analytic Techniques


Conclusions and Future Outlook

Fraud Detection in Healthcare; Varun Chandola, Jack Schryver, and Sreenivas Sukumar


Understanding Fraud in the Healthcare System

Definition and Types of Healthcare Fraud

Identifying Healthcare Fraud from Data

Knowledge Discovery-Based Solutions for Identifying Fraud


Data Analytics for Pharmaceutical Discoveries; Shobeir Fakhraei, Eberechukwu Onukwugha, and Lise Getoor


Chemical andBiologicalData

Spontaneous Reporting Systems (SRSs)

Electronic Health Records

Patient-Generated Data on the Internet

Biomedical Literature

Summary and Future Challenges

Clinical Decision Support Systems; Martin Alther and Chandan K. Reddy


Historical Perspective

Various Types of CDSS

Decision Support during Care Provider Order Entry

Diagnostic Decision Support

Human-Intensive Techniques

Challenges of CDSS

Legal and Ethical Issues


Computer-Assisted Medical Image Analysis Systems; Shu Liao, Shipeng Yu, Matthias Wolf, Gerardo Hermosillo, Yiqiang Zhan, Yoshihisa Shinagawa, Zhigang Peng, Xiang Sean Zhou, Luca Bogoni, and Marcos Salganicoff


Computer-Aided Diagnosis/Detection of Diseases

Medical Imaging Case Studies


Mobile Imaging and Analytics for Biomedical Data; Stephan M. Jonas and Thomas M. Deserno


Image Formation

Data Visualization

Image Analysis

Image Management and Communication


About the Editors

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."

About the Series

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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Subject Categories

BISAC Subject Codes/Headings:
BUSINESS & ECONOMICS / Industries / Service Industries
COMPUTERS / Programming / Games
COMPUTERS / Database Management / Data Mining
MEDICAL / Administration