OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.

A new title in the acclaimed Understanding series that focuses on the science of healthcare delivery Over the past decade, the subject of Systems Science has skyrocketed in importance in the healthcare field. With its engaging, clinically relevant style, Understanding Healthcare Delivery Science is the perfect introduction to this timely topic. It covers every aspect of what actually constitutes “best care” and how it can be most efficiently delivered from an operational standpoint. The book is exceptional for two other reasons: numerous case vignettes put the content in a clinically relevant framework, and its comprehensive coverage spans everything from quality and safety to data and policy. Readers will find a valuable opening section that delivers an outstanding introductory discussion of Healthcare Delivery Science Co-author Dr. Michael Howell is a nationally recognized expert on healthcare quality, whose research has been covered by The New York Times, CNN, and Consumer Reports. He has served on national quality- and safety-related national advisory panels for the CDC, Society of Critical Care Medicine, CMS, and others. An active healthcare delivery scientist, Dr. Howell has published more than 90 research articles, editorials, and book chapters on topics related to quality, safety, patient-centeredness, and critical care.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling•Understand how use ML tools in real world business problems, where causation matters more that correlation•Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.

"This set offers thorough examination of the issues of importance in the rapidly changing field of data warehousing and mining"--Provided by publisher.