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Full Description
This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data.
The book has been updated with the latest research in massive data, machine learning, and AI ethics. It covers new topics in managing the complexities of massive data, and provides examples of complex machine learning models. Updated case studies from global healthcare providers showcase the use of big data and AI in the fight against chronic and novel diseases, including COVID-19. The ethical implications of digital healthcare, analytics, and the future of AI in population health management are explored. You will learn how to create a machine learning model, evaluate its performance, and operationalize its outcomes within your organization. Case studies from leading healthcare providers cover scaling global digital services. Techniques are presented to evaluate the efficacy, suitability, and efficiency of AI machine learning applications through case studies and best practice, including the Internet of Things.
You will understand how machine learning can be used to develop health intelligence-with the aim of improving patient health, population health, and facilitating significant care-payer cost savings.
What You Will Learn
Understand key machine learning algorithms and their use and implementation within healthcare
Implement machine learning systems, such as speech recognition and enhanced deep learning/AI
Manage the complexities of massive data
Be familiar with AI and healthcare best practices, feedback loops, and intelligent agents
Who This Book Is For
Health care professionals interested in how machine learning can be used to develop health intelligence - with the aim of improving patient health, population health and facilitating significant care-payer cost savings.
Contents
Chapter 1: What Is Artificial Intelligence?.- Chapter 2: Data.- Chapter 3: What Is Machine Learning.- Chapter 4: Machine Learning Algorithms.- Chapter 5: How to Perform Machine Learning.- Chapter 6: Preparing Data.- Chapter 7: Evaluating Machine Learning Models.- Chapter 8: Machine Learning and AI Ethics.- Chapter 9: The Future of Healthcare.- Chapter 10: Case Studies.- Appendix A: References.- Appendix B: Glossary.
