Data Warehousing for Biomedical Informatics is a step-by-step how-to guide for designing and building an enterprise-wide data warehouse across a biomedical or healthcare institution, using a four-iteration lifecycle and standardized design pattern. It enables you to quickly implement a fully-scalable generic data architecture that supports your org

One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.

This comprehensive volume demonstrates the broad scope of uses for data mining and includes detailed strategies and methodologies for analyzing data from biomedical images, signals, and systems. Written by experts in the field, it presents data mining techniques in the context of various important clinical issues, including diagnosis and grading of depression, identification and classification of arrhythmia and ischemia, and description of classification paradigms for mammograms. The book provides ample information and techniques to benefit researchers, practitioners, and educators of biomedical science and engineering.

Comprehensively presents the foundations and leading application research in medical informatics/biomedicine. The concepts and techniques are illustrated with detailed case studies. Authors are widely recognized professors and researchers in Schools of Medicine and Information Systems from the University of Arizona, University of Washington, Columbia University, and Oregon Health & Science University. Related Springer title, Shortliffe: Medical Informatics, has sold over 8000 copies The title will be positioned at the upper division and graduate level Medical Informatics course and a reference work for practitioners in the field.

This edition is a straightforward view of a clinical data warehouse development project, from Inception through Implementation and follow-up. Through first-hand experiences from Individuals charged with the Implementation, this book offers guidance and multiple perspectives on the data warehouse development process--from the Initial vision to system-wide release. The book provides valuable lessons learned during a data warehouse Implementation at King Faisal Specialist Hospital and Research Center (KFSH & RC) in Riyadh, Saudi Arabia, a large, modern, tertiary-care hospital with an IT environment that parallels a typical U.S. hospital.

This is the first book to offer a comprehensive yet concise overview of the challenges and opportunities presented by the use of big data in healthcare. The respective chapters address a range of aspects: from health management to patient safety; from the human factor perspective to ethical and economic considerations, and many more. By providing a historical background on the use of big data, and critically analyzing current approaches together with issues and challenges related to their applications, the book not only sheds light on the problems entailed by big data, but also paves the way for possible solutions and future research directions. Accordingly, it offers an insightful reference guide for health information technology professionals, healthcare managers, healthcare practitioners, and patients alike, aiding them in their decision-making processes; and for students and researchers whose work involves data science-related research issues in healthcare.

Advances in the biomedical sciences, especially genomics, proteomics, and metabolomics, taken together with the expanding use of electronic health records, are radically changing the IT infrastructure and software applications needed to support the transfer of knowledge from bench to bedside. Pediatric Biomedical Informatics: Computer Applications in Pediatric Research describes the core resources in informatics necessary to support biomedical research programs and how these can best be integrated with hospital systems to receive clinical information that is necessary to conduct translational research.The focus is on the authors’ recent practical experiences in establishing an informatics infrastructure in a large research-intensive children’s hospital. This book is intended for translational researchers and informaticians in pediatrics, but can also serve as a guide to all institutions facing the challenges of developing and strengthening informatics support for biomedical research. The first section of the book discusses important technical challenges underlying computer-based pediatric research, while subsequent sections discuss informatics applications that support biobanking and a broad range of research programs. Pediatric Biomedical Informatics provides practical insights into the design, implementation, and utilization of informatics infrastructures to optimize care and research to benefit children. Dr. John Hutton is the Vice President and Director of Biomedical Informatics at Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA. He is also Professor of Pediatrics and Associate Dean for Information Services at the University of Cincinnati College of Medicine.

An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.

This trailblazing resource on biomedical informatics provides medical researchers with innovative techniques for integrating and federating data from clinical and molecular studies. This volume helps researchers manage data, expedite their efforts, and make the most of targeted basic research.

This third edition to the award-winning book is a straightforward view of a clinical data warehouse development project, from inception through implementation and follow-up. Through first-hand experiences from individuals charged with such an implementation, this book offers guidance and multiple perspectives on the data warehouse development process – from the initial vision to system-wide release. The book provides valuable lessons learned during a data warehouse implementation at King Faisal Specialist Hospital and Research Center (KFSH&RC) in Riyadh, Saudi Arabia – a large, modern, tertiary-care hospital with an IT environment that parallels a typical U.S. hospital. This book also examines the value of the data warehouse from the perspectives of a large healthcare system in the U.S. and a corporate health services business unit. Special features of the book include a sample RFP, data warehouse project plan, and information analysis template. A helpful glossary and acronyms list are included.