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Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes
Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges.
You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization.
Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.
What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AISelect learning methods/algorithms and tuning for use in healthcareRecognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho 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.
You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization.
Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.
What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AISelect learning methods/algorithms and tuning for use in healthcareRecognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho 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.
409 pages, Kindle Edition
Published February 4, 2019
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