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Health Informatics Data Analysis

Methods and Examples

  • Book
  • © 2017

Overview

Editors:
  1. Dong Xu

    1. Digital Biology Laboratory, Computer Science Department, University of Missouri-Columbia, Columbia, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. May D. Wang

    1. Georgia Institute of Technology and Emory University, Atlanta, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Fengfeng Zhou

    1. College of Computer Science and Technology, Jilin University, Changchun, China

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  4. Yunpeng Cai

    1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

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    You can also search for this editor in PubMed   Google Scholar

  • Offers a comprehensive overview of biomedical data types and explains why and how to examine types of data
  • Chapters provide both field overviews and sample analysis
  • Examines OMICs, an increasingly critical part of health informatics with official FDA recognition and a wide range of diagnosis applications
  • Includes supplementary material: sn.pub/extras

Part of the book series: Health Information Science (HIS)

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Global Nonlinear Fitness Function for Protein Structures

    • Yun Xu, Changyu Hu, Yang Dai, Jie Liang
    Pages 1-35

  3. Computational Methods for Mass Spectrometry Imaging: Challenges, Progress, and Opportunities

    • Chanchala D. Kaddi, May D. Wang
    Pages 37-49

  4. Identification and Functional Annotation of LncRNAs in Human Disease

    • Qi Liao, Dechao Bu, Liang Sun, Haitao Luo, Yi Zhao
    Pages 51-60

  5. Metabolomics Characterization of Human Diseases

    • Masahiro Sugimoto
    Pages 61-71

  6. Metagenomics for Monitoring Environmental Biodiversity: Challenges, Progress, and Opportunities

    • Raghu Chandramohan, Cheng Yang, Yunpeng Cai, May D. Wang
    Pages 73-87

  7. Clinical Assessment of Disease Risk Factors Using SNP Data and Bayesian Methods

    • Ivan Kozyryev, Jing Zhang
    Pages 89-102

  8. Imaging Genetics: Information Fusion and Association Techniques Between Biomedical Images and Genetic Factors

    • Dongdong Lin, Vince D. Calhoun, Yu-Ping Wang
    Pages 103-114

  9. Biomedical Imaging Informatics for Diagnostic Imaging Marker Selection

    • Sonal Kothari Phan, Ryan Hoffman, May D. Wang
    Pages 115-127

  10. ECG Annotation and Diagnosis Classification Techniques

    • Yan Yan, Xingbin Qin, Lei Wang
    Pages 129-154

  11. EEG Visualization and Analysis Techniques

    • Gregor Schreiber, Hong Lin, Jonathan Garza, Yuntian Zhang, Minghao Yang
    Pages 155-168

  12. Big Health Data Mining

    • Chao Zhang, Shunfu Xu, Dong Xu
    Pages 169-184

  13. Computational Infrastructure for Telehealth

    • Fedor Lehocki, Igor Kossaczky, Martin Homola, Marek Mydliar
    Pages 185-199

  14. Healthcare Data Mining, Association Rule Mining, and Applications

    • Chih-Wen Cheng, May D. Wang
    Pages 201-210
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Keywords

About this book

This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection.


With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. 


This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.









Reviews

“It would be nice to enhance the examples presented throughout the book with a more detailed step-by-step guide for non-experts in the medical domain, including a reader-friendly analysis and interpretation of the results obtained along with a summary of lessons learned.” (Sergio Ilarri, Computing Reviews, April, 2018)​

Editors and Affiliations

  • Digital Biology Laboratory, Computer Science Department, University of Missouri-Columbia, Columbia, USA

    Dong Xu

  • Georgia Institute of Technology and Emory University, Atlanta, USA

    May D. Wang

  • College of Computer Science and Technology, Jilin University, Changchun, China

    Fengfeng Zhou

  • Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yunpeng Cai

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