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AI for Healthcare with Keras and Tensorflow 2.0 - Design, Develop, and Deploy Machine Learning Models Using Healthcare Data (Paperback, 1st ed.)
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AI for Healthcare with Keras and Tensorflow 2.0 - Design, Develop, and Deploy Machine Learning Models Using Healthcare Data (Paperback, 1st ed.)
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Learn how AI impacts the healthcare ecosystem through real-life
case studies with TensorFlow 2.0 and other machine learning (ML)
libraries. This book begins by explaining the dynamics of the
healthcare market, including the role of stakeholders such as
healthcare professionals, patients, and payers. Then it moves into
the case studies. The case studies start with EHR data and how you
can account for sub-populations using a multi-task setup when you
are working on any downstream task. You also will try to predict
ICD-9 codes using the same data. You will study transformer models.
And you will be exposed to the challenges of applying modern ML
techniques to highly sensitive data in healthcare using federated
learning. You will look at semi-supervised approaches that are used
in a low training data setting, a case very often observed in
specialized domains such as healthcare. You will be introduced to
applications of advanced topics such as the graph convolutional
network and how you can develop and optimize image analysis
pipelines when using 2D and 3D medical images. The concluding
section shows you how to build and design a closed-domain Q&A
system with paraphrasing, re-ranking, and strong QnA setup. And,
lastly, after discussing how web and server technologies have come
to make scaling and deploying easy, an ML app is deployed for the
world to see with Docker using Flask. By the end of this book, you
will have a clear understanding of how the healthcare system works
and how to apply ML and deep learning tools and techniques to the
healthcare industry. What You Will Learn Get complete, clear, and
comprehensive coverage of algorithms and techniques related to case
studies Look at different problem areas within the healthcare
industry and solve them in a code-first approach Explore and
understand advanced topics such as multi-task learning,
transformers, and graph convolutional networks Understand the
industry and learn ML Who This Book Is For Data scientists and
software developers interested in machine learning and its
application in the healthcare industry
General
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