Book Image

Applied Machine Learning for Healthcare and Life Sciences Using AWS

By : Ujjwal Ratan
Book Image

Applied Machine Learning for Healthcare and Life Sciences Using AWS

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)
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 gained an understanding of the medical device industry and the regulatory aspects of the industry designed to ensure the safe usage of these devices among patients. We also looked at the radiology image workflow and the various components involved in the system to make it work. We saw how ML models, when applied to medical devices, can improve the overall health of the population and prevent serious medical events. In the final sections of the chapter, we got an introduction to SageMaker training and went through an implementation exercise to train an ML model to identify a normal chest X-ray compared to one displaying pneumonia.

In Chapter 7, Applying Machine Learning to Genomics, we will look at how ML technology is transforming the world of clinical research. We will understand the role of genomic sequencing in precision medicine and look at examples that demonstrate why it’s important to consider genomic data in clinical diagnosis.