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Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes
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 healthcareImplement machine learning systems, such as speech recognition and enhanced deep learning/AIManage the complexities of massive dataBe familiar with AI and healthcare best practices, feedback loops, and intelligent agents
Who This Book Is ForHealth 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.
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 healthcareImplement machine learning systems, such as speech recognition and enhanced deep learning/AIManage the complexities of massive dataBe familiar with AI and healthcare best practices, feedback loops, and intelligent agents
Who This Book Is ForHealth 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.
442 pages, Kindle Edition
Published December 15, 2020
15 people are currently reading
58 people want to read
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Displaying 1 of 1 review
March 24, 2019
After the first chapter of this book, I was ready to put it down and regret the money I spent on it. It seemed to walk over ground that I've already covered as a researcher in medical informatics. Fortunately, I continued, for I came to learn a lot from this author. Although not as succinctly written as academic papers, this book is thoroughly researched and comments on an emerging field - the intersection of healthcare and software. It also comments on this from a British perspective. I am used to reading Americans comment on this field, but comments from a Brit who possesses experience in the field is particularly interesting to me.
The author's experience in this field is particular to Type-2 Diabetes. It is quite obvious that his research tilts towards diabetes. I would like to hear more from this author about work that's being done on other major diseases like HIV/AIDS, malaria, emerging diseases, cystic fibrosis, etc. That is a tall order to ask, I understand, and much work needs to be done for this to be the case. Nonetheless, this is the broad frontier that we now face between medicine and computers.
I'm glad that Panesar added his voice to the effort to leverage computers to fight disease, and I'm glad that I took the time to listen.
The author's experience in this field is particular to Type-2 Diabetes. It is quite obvious that his research tilts towards diabetes. I would like to hear more from this author about work that's being done on other major diseases like HIV/AIDS, malaria, emerging diseases, cystic fibrosis, etc. That is a tall order to ask, I understand, and much work needs to be done for this to be the case. Nonetheless, this is the broad frontier that we now face between medicine and computers.
I'm glad that Panesar added his voice to the effort to leverage computers to fight disease, and I'm glad that I took the time to listen.
Displaying 1 of 1 review