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:
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Healthcare Data Sources and Basic Analytics - details the various healthcare data sources and analytical techniques used in the processing and analysis of such data
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Advanced Data Analytics for Healthcare - covers advanced analytical methods, including clinical prediction models, temporal pattern mining methods, and visual analytics
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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.
Editor Biographies
Contributors
Preface
Chapter 1 : An Introduction to Healthcare Data Analytics
Part I : Healthcare Data Sources and Basic Analytics
Chapter 2 : Electronic Health Records : A Survey
Chapter 3 : Biomedical Image Analysis
Chapter 4 : Mining of Sensor Data in Healthcare : A Survey
Chapter 5 : Biomedical Signal Analysis
Chapter 6 : Genomic Data Analysis for Personalized Medicine
Chapter 7 : Natural Language Processing and Data Mining for Clinical Text
Chapter 8 : Mining the Biomedical Literature
Chapter 9 : Social Media Analytics for Healthcare
Part II : Advanced Data Analytics for Healthcare
Chapter 10 : A Review of Clinical Prediction Models
Chapter 11 : Temporal Data Mining for Healthcare Data
Chapter 12 : Visual Analytics for Healthcare
Chapter 13 : Predictive Models for Integrating Clinical and Genomic Data
Chapter 14 : Information Retrieval for Healthcare
Chapter 15 : Privacy-Preserving Data Publishing Methods in Healthcare
Part III : Applications and Practical Systems for Healthcare
Chapter 16 : Data Analytics for Pervasive Health
Chapter 17 : Fraud Detection in Healthcare
Chapter 18 : Data Analytics for Pharmaceutical Discoveries
Chapter 19 : Clinical Decision Support Systems
Chapter 20 : Computer-Assisted Medical Image Analysis Systems
Chapter 21 : Mobile Imaging and Analytics for Biomedical Data
Index
