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Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using Artificial Intelligence (AI), Machine Learning (ML), and Related Technologies, Second Edition discusses the needs of healthcare and medicine in the 21st century, explaining how data analytics play an important and revolutionary role. With healthcare effectiveness and economics facing growing challenges, there is a rapidly emerging movement to fortify medical treatment and administration by tapping the predictive power of Big Data, such as predictive analytics, which can bolster patient care, reduce costs, and deliver greater efficiencies across a wide range of operational functions.
Sections bring a historical perspective, highlight the importance of using predictive analytics to help solve health crisis such as the COVID-19 pandemic, provide access to practical step-by-step tutorials and case studies online, and use exercises based on real-world examples of successful predictive and prescriptive tools and systems. The final part of the book focuses on specific technical operations related to quality, cost-effective medical and nursing care delivery and administration brought by practical predictive analytics.
The “Tutorials and Case Studies” (only on the companion web site for this 2nd Edition book) illustrate the use of predictive analytic software from various sources. You, the reader, can oftentimes obtain “free evaluations” of software and/or low-priced academic copies of software to use in working through these tutorials, if you wish to study and replicate them. Some of the tutorials use TIBCO-STATISTICA, others use SAS-Enterprise Miner or R-Rattle, and others use KNIME. This software is not provided on this book’s companion web page, but you can use a search engine to find the SAS, KNIME, and R websites and download what is available or find out how to get an evaluation copy (if available). In fact, in the tutorial using R-Rattle, instructions are provided on how to do this (realizing that the specific URL may have changed by the time you are reading this, and you will have to search further). KNIME and R are “freeware” or have free versions available to download from their web pages.
The purpose of this chapter is to discuss prediction tool development. Steps of a general data pipeline are discussed, including data collection, data tidying, exploratory data analysis, modeling, and model validation. We also endorse the incorporation of the R Shiny web app and connect to SQL databases to enhance data workflows and efficiency. The need for clinically relevant prediction models is highlighted with an emphasis on the importance of transparency by following the standardized reporting methods of the TRIPOD guidelines.
Brings a historical perspective in medical care to discuss both the current status of health care delivery worldwide and the importance of using modern predictive analytics to help solve the health care crisis
Provides online tutorials on several predictive analytics systems to help readers apply their knowledge on today’s medical issues and basic research
Teaches how to develop effective predictive analytic research and to create decisioning/prescriptive analytic systems to make medical decisions quicker and more accurate