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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

Summary

In this chapter, we looked at the concepts of operational efficiency in healthcare and why is it important for providers to pay attention to it. We then looked into two important areas of automation in healthcare – clinical document processing and voice-based applications. Each of these areas consumes a lot of time, as they require manual intervention for processing and an understanding of the clinical information embedded within them. We looked at some common methods of automating the extraction of clinical information from these unstructured data modalities and processing them to create a longitudinal view of a patient, a vital asset to have for the applications of clinical analytics and ML. Lastly, we built an example application to transcribe a clinical dictation using Amazon Transcribe Medical and then process that transcription using Amazon Comprehend Medical to extract clinical entities into a structured row or column format.

In Chapter 5, Implementing Machine...

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