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.
An Introduction to Healthcare Data Analytics; Chandan K. Reddy and Charu C. Aggarwal Introduction Healthcare Data Sources and Basic Analytics Advanced Data Analytics for Healthcare Applications and Practical Systems for Healthcare Resources for Healthcare Data Analytics Conclusions HEALTHCARE DATA SOURCES AND BASIC ANALYTICS Electronic Health Records: A Survey; Rajiur Rahman and Chandan K. Reddy Introduction History of EHR Components of HER Coding Systems Benefits of EHR Barriers to Adopting EHR Challenges of Using EHR Data Phenotyping Algorithms Conclusions Biomedical Image Analysis; Dirk Padfield, Paulo Mendonca, and Sandeep Gupta Introduction 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 Introduction 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 Introduction Types of Biomedical Signals ECG Signal Analysis. Denoising of Signals Multivariate Biomedical Signal Analysis Cross-Correlation Analysis Recent Trends in Biomedical Signal Analysis Discussions Genomic Data Analysis for Personalized Medicine; Juan Cui Introduction 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 Introduction Natural Language Processing Mining Information from Clinical Text Challenges of Processing Clinical Reports Clinical Applications Conclusions Mining the Biomedical Literature; Claudiu Mihaila, Riza Batista-Navarro, Noha Alnazzawi, Georgios Kontonatsios, Ioannis Korkontzelos, Rafal Rak, Paul Thompson, and Sophia Ananiadou Introduction Resources Terminology Acquisition and Management InformationExtraction Discourse Interpretation Text Mining Environments Applications Integration with Clinical Text Mining Conclusions Social Media Analytics for Healthcare; Alexander Kotov Introduction 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 ADVANCED DATA ANALYTICS FOR HEALTHCARE A Review of Clinical Prediction Models; Chandan K. Reddy and Yan Li Introduction Basic Statistical Prediction Models Alternative Clinical Prediction Models Survival Models Evaluation and Validation Conclusion Temporal Data Mining for Healthcare Data; Iyad Batal Introduction Association Analysis Temporal Pattern Mining Sensor Data Analysis Other Temporal Modeling Methods Resources Summary Visual Analytics for Healthcare; David Gotz, Jesus Caban, and Annie T. Chen Introduction Introduction to Visual Analytics and Medical Data Visualization Visual Analytics in Healthcare Conclusion Predictive Models for Integrating Clinical and Genomic Data; Sanjoy Dey, Rohit Gupta, Michael Steinbach, and Vipin Kumar Introduction Issues and Challenges in Integrating Clinical and Genomic Data Different Types of Integration Different Goals of Integrative Studies Validation Discussion and Future Work Information Retrieval for Healthcare; William R. Hersh Introduction Knowledge-Based Information in Healthcare and Biomedicine Content of Knowledge-Based Information Resources Indexing Retrieval Evaluation Research Directions Conclusion Privacy-Preserving Data Publishing Methods in Healthcare; Yubin Park and Joydeep Ghosh Introduction Data Overview and Preprocessing Privacy-Preserving Publishing Methods Challenges with Health Data Conclusion APPLICATIONS AND PRACTICAL SYSTEMS FOR HEALTHCARE Data Analytics for Pervasive Health; Giovanni Acampora, Diane J. Cook, Parisa Rashidi, and Athanasios V. Vasilakos Introduction Supporting Infrastructure and Technology Basic Analytic Techniques Advanced Analytic Techniques Applications Conclusions and Future Outlook Fraud Detection in Healthcare; Varun Chandola, Jack Schryver, and Sreenivas Sukumar Introduction Understanding Fraud in the Healthcare System Definition and Types of Healthcare Fraud Identifying Healthcare Fraud from Data Knowledge Discovery-Based Solutions for Identifying Fraud Conclusions Data Analytics for Pharmaceutical Discoveries; Shobeir Fakhraei, Eberechukwu Onukwugha, and Lise Getoor Introduction 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 Introduction 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 Conclusion 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 Introduction Computer-Aided Diagnosis/Detection of Diseases Medical Imaging Case Studies Conclusions Mobile Imaging and Analytics for Biomedical Data; Stephan M. Jonas and Thomas M. Deserno Introduction Image Formation Data Visualization Image Analysis Image Management and Communication Index