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Applied Machine Learning for Healthcare and Life Sciences using AWS

Applied Machine Learning for Healthcare and Life Sciences using AWS

By : Ujjwal Ratan
4.9 (14)
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Applied Machine Learning for Healthcare and Life Sciences using AWS

Applied Machine Learning for Healthcare and Life Sciences using AWS

4.9 (14)
By: Ujjwal Ratan

Overview of this book

While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics. This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You’ll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications. By the end of this book, you’ll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence.
Table of Contents (19 chapters)
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1
Part 1: Introduction to Machine Learning on AWS
Free Chapter
2
Chapter 1: Introducing Machine Learning and the AWS Machine Learning Stack
4
Part 2: Machine Learning Applications in the Healthcare Industry
9
Part 3: Machine Learning Applications in the Life Sciences Industry
14
Part 4: Challenges and the Future of AI in Healthcare and Life Sciences

To get the most out of this book

The exercises in this book require an AWS account and the necessary steps to configure the AWS Python SDK and the AWS Command Line Interface (CLI). You will run the examples from the AWS CLI or a Jupyter notebook from a Sagemaker notebook instance or the Sagemaker Studio environment.

Software/hardware covered in the book

Operating system requirements

Python 3.X

Windows, macOS, or Linux

AWS SDK for Python (Boto 3)

AWS Command Line Interface (CLI)

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Note

The exercises in this book use publicly available datasets. Before using these services on any other dataset, please review the AWS HIPPA eligibility guidelines for the respective service. You can learn more about AWS HIPPA guidelines at https://aws.amazon.com/compliance/hipaa-compliance/.

There may be some costs associated with running the example exercises at the end of the chapters. Please follow all best practices around cost optimizations to ensure you are keeping the costs to a minimum. You can learn more about AWS cost optimization at https://aws.amazon.com/architecture/cost-optimization/?cards-all.sort-by=item.additionalFields.sortDate&cards-all.sort-order=desc&awsf.content-type=*all&awsf.methodology=*all.

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