This book offers concepts, methods and a framework to analyze healthcare data in new ways to improve patient health outcomes, improve population health and reduce costs. The framework takes the reader step-by-step through data warehouse and data management architectures, analytics algorithms and modeling techniques for applications in predictive medicine, optimization, machine learning, natural language processing, classification and data clustering. This book introduces the reader to data science for healthcare. It provides a clinical perspective to data including sources of open healthcare data, clinical prediction rules and much more.

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Peter K. Ghavami received his PhD in Industrial and Systems Engineering from University of Washington in Seattle and his M.S. in Engineering Management from Portland State University. His career started as a software engineer, with progressive responsibilities at IBM Corp., Director of engineering, Chief Scientist, VP of Engineering and Product Management at various high technology firms. He was Director of Informatics at UW Medicine leading numerous clinical system implementations and new product development projects. He has been strategic advisor and acting VP of Informatics for various analytics companies. His research in data analytics has focused on Prognostics, the application of analytics to predict failures in systems, and has applied this approach to predictive medicine. He has authored several papers and book chapters on software process improvement, super computing, distributed network architectures, and software quality. His first book, titled Lean, Agile and Six Sigma Information Technology Management was published in 2008. Peter is on the advisory board of several clinical analytics companies and often invited as lecturer and speaker on this topic. He is a member of IEEE Reliability Society, and an active member of HIMSS and a contributor to the Data Analytics Task Force and a member of the HIMSS Clinical and Business Intelligence Committee.

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