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Product details
File Size: 15804 KB
Print Length: 210 pages
Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
Publisher: CRC Press; 1 edition (February 15, 2017)
Publication Date: February 15, 2017
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B06XPNZZCZ
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Amazon Best Sellers Rank:
#1,410,037 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
It is a collection of barely academic quality papers that do not give enough detail. Frameworks are proprietary and names are not mentioned, technology or results are not really discussed.
Great primer for those of us who are new to the world of big data in the healthcare domain. I was curious as to how these powerful tools apply in a clinical setting and how they can impact research to improve individual patient treatment methods and outcomes. The book goes over how tools such as Hadoop and GPU chips are used, outlines concepts such as neural networks, and highlights the use of software delivery methods such as Agile are used to implement big data solutions. The case studies were especially interesting as they highlighted across payer and provider.
As an engineer in machine learning, this book seems to me that it addresses in a spectacular way all the dimensions to take into account in the construction of analytical solutions in health, there are many things that I have thought intuitively, but this book has allowed me to confirm some Of my hypothesis, I am very happy to have been able to read it, it has been very useful in my work and in my passion to improve in the area of health, a great congratulation to all those who made possible its creation and publication.
This is what exactly the doctor ordered for advanced analytics sphere. Without such lucidly written books, advanced analytics in healthcare is at risk from issues related to hype. I enjoyed the book and I am confident it will help me in my work with Analytics1.0/3.0 strategy in my career.
This book is a must-read for Healthcare IT professionals interested in Machine Learning, AI and Big Data in Healthcare. The authors have done a great job putting together real-life examples of various healthcare providers in US including Integrated Delivery Network(IDN), Academic Medical Center(AMC). These case studies provide useful perspectives on the current state of big data,machine learning and AI - and what the near future holds for healthcare professionals and policy makers. There are eight case studies in this book which represents not only what's reality today but also what's coming. I strongly recommend this book to those who involved or plan to be part of Healthcare Analytics projects.
I currently work on the technical side of the Big Data space and did some research work in the clinical setting and could not put this book down once I started reading it! My favorite part is the real-life case studies that the authors go through. The authors sum things up pretty well that the key to any successful big data analytics project is having a strongly defined business use-case that can show some tangible ROI. And the use-cases that he goes through show exactly that. You won't find details about big data projects in the healthcare space like the ones in this book. The NRF Framework provides a great set of points for any data scientist or business user to think about before beginning a big data project. I'm definitely going to be using it with my internal and external customers. And don't fret if you aren't super well-versed in machine learning terminology. The author does a phenomenal job at providing the readers with a way of thinking about what kind of models are applied in different use-cases and what the tradeoffs of using those models are! Whether you are new to the healthcare analytics space or have been in the field for a while, you'll find your money's worth!
A great exposition on machine learning, big data, and analytics for novices & experts alike. Essential (and easy) reading for anyone involved in IoT, fast/big data, NLP, and AI. The authors' writing is crisp & clear and the book is filled with nice examples & case studies. Strongly recommended. A pricey purchase but more than worth it. Hope the book's price comes down in future editions :)
This is an exceptionally insightful book that explains the future of big data and machine learning in healthcare. Author Prashant Natarajan has written it in a tone which is comprehensible, yet detailed. The publication provides the reader with a deeper understanding of analytics and what is required to ensure that health data is generated from both quantitative and qualitative sources for AI. The content is suitable for all stakeholders, including HCP's and patients seeking to gain a more meaningful understanding of digital medicine.
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