Overview
- Offers healthcare professionals a tech jargon-free understanding of the applications of machine learning in healthcare
- Covers the ethics of data and learning governance and the hurdles that require addressing to achieve a long-term gain from machine learning and AI
- Written by an award-winning researcher of intelligent systems that improve user experience through collaboration, machine learning, and data mining
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Table of contents (10 chapters)
Keywords
About this book
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.
Authors and Affiliations
About the author
Arjun Panesar is the founder of Diabetes Digital Media (DDM), the world’s largest diabetes community and provider of evidence-based digital health interventions. He holds an honors degree (MEng) in computing and artificial intelligence from Imperial College, London. He has a decade of experience in big data and affecting user outcomes, and leads the development of intelligent, evidence-based digital health interventions that harness the power of big data and machine learning to provide precision patient care to patients, health agencies, and governments worldwide.
Arjun’s work has received international recognition and was featured by the BBC, Forbes, New Scientist, and The Times. He has received innovation, business, and technology awards, including being named the top app for prevention of type 2 diabetes.
Arjun is an advisor to the Information School, at the University of Sheffield, Fellow to the NHS Innovation Accelerator, and was recognized by Imperial College as an Emerging Leader in 2020 for his contribution and impact to society.
Bibliographic Information
Book Title: Machine Learning and AI for Healthcare
Book Subtitle: Big Data for Improved Health Outcomes
Authors: Arjun Panesar
DOI: https://doi.org/10.1007/978-1-4842-6537-6
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Arjun Panesar 2021
Softcover ISBN: 978-1-4842-6536-9Published: 16 December 2020
eBook ISBN: 978-1-4842-6537-6Published: 15 December 2020
Edition Number: 2
Number of Pages: XXX, 407
Number of Illustrations: 61 b/w illustrations
Topics: Machine Learning, Professional Computing