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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Open the terminal or CLI on your computer and navigate to the directory where you have the transcribe_text.py file.”

A block of code is set as follows:

from sagemaker.workflow.pipeline import Pipeline
pipeline_name = f"mypipeline"

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

from sagemaker.workflow.parameters import (
    ParameterInteger,
    ParameterString,
    ParameterFloat,
    ParameterBoolean
)

Any command-line input or output is written as follows:

$cd SageMaker
$git clone https://github.com/PacktPublishing/Applied-Machine-Learning-for-Healthcare-and-Life-Sciences-using-AWS.git

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Select System info from the Administration panel.”

Tips or important notes

Appear like this.