1st Edition

Healthcare Data Analytics

ISBN 9781482232110
Published June 23, 2015 by Chapman and Hall/CRC
760 Pages 193 B/W Illustrations

USD $115.00

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Book 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.

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


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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."


"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