E-Book, Englisch, 224 Seiten
Ratan Applied Machine Learning for Healthcare and Life Sciences using AWS
1. Auflage 2024
ISBN: 978-1-80461-919-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection
Transformational AI implementations for biotech, clinical, and healthcare organizations
E-Book, Englisch, 224 Seiten
ISBN: 978-1-80461-919-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Table of Contents - Introducing Machine Learning and the AWS Machine Learning Stack
- Exploring Key AWS Machine Learning Services for Healthcare and Life Sciences
- Machine Learning for Patient Risk Stratification
- Using Machine Learning to Improve Operational Efficiency for Healthcare Providers
- Implementing Machine Learning for Healthcare Payors
- Implementing Machine Learning for Medical Devices and Radiology Images
- Applying Machine Learning to Genomics
- Applying Machine Learning to Molecular Data
- Applying Machine Learning to Clinical Trials and Pharmacovigilance
- Utilizing Machine Learning in the Pharmaceutical Supply Chain
- Understanding Common Industry Challenges and Solutions
- Understanding Current Industry Trends and Future Applications
Preface
We have seen multiple ways in which AI is touching our lives in a meaningful way. It’s almost like having a superpower that you never knew you needed, but now that you have it, you can see how it is changing our lives for the better. Automation driven by AI is helping organizations achieve levels of operational efficiency they never thought were possible, which in turn is boosting economies. The accessibility of cutting-edge infrastructure and models has improved tremendously with the power of cloud computing, which has democratized AI by putting it in the hands of everyone. It is no wonder that the last decade has seen the use of AI in the healthcare and life sciences industry increase massively. As the industry is undergoing a transformation driven by technology and digitization, it produces large volumes of data in multiple modalities. To utilize the full potential of this data, organizations are applying machine learning to process, analyze, and interpret critical information from these datasets to improve and save the lives of patients. It is helping improve provider efficiencies and improve care quality, and is bringing the costs of drugs and therapies down.
This book will help you understand how this is happening. It will introduce you to the different verticals of the healthcare and life sciences industry such as providers, payors, pharmaceuticals, genomics, and medical imaging. It begins by introducing you to the concept of machine learning and then progresses to show how you can apply machine learning to workloads in each of these industry verticals. The book gradually builds your Amazon Web Services (AWS) machine learning knowledge. You will be introduced to low-code AI services from AWS and each chapter progresses to more advanced topics. The exercises at the end of the chapters are designed for you to practice what you learned and apply the learning to an actual problem in the industry vertical. I hope you enjoy this ride and find what you learn from this book valuable for a long time to come.
Who this book is for
This book will help you build an understanding of the healthcare and life sciences industry, machine learning and deep learning, and AWS machine learning services. Business and technology decision-makers will see how machine learning is transforming the industry and the role AWS is playing in that. Developers, data scientists, and machine learning engineers will learn about AWS machine learning services and how they can be applied to solve problems in different verticals of the healthcare and life sciences industry. The practical exercises will solidify your knowledge of the concepts learned in each chapter.
What this book covers
, , covers the basic concepts of machine learning and how it differs from a traditional software application.
, , dives into some key machine learning services from AWS that are critical for healthcare and life sciences industries. This chapter will give you an introduction to these services, their key APIs, and some usage examples.
, , explains the concept of risk stratification of patients. It shows how common machine learning algorithms for classification and regression tasks can be applied to identify at-risk patients.
, , covers operational efficiency in healthcare and why it is important. You will also learn about two common applications of machine learning to improve operational efficiency for healthcare providers.
, , introduces you to the healthcare payor industry. You will get an understanding of how health insurance organizations process claims.
, , introduces you to the medical device industry. It goes into the details of various regulatory requirements for medical devices to be approved for use based on the type of medical device.
, , explores the world of genomes and the evolution of genomic sequencing. We will see how genomic data interpretation and analysis is changing the world of medicine.
, , introduces molecular data and its interpretation. We will learn about the process of the discovery of new drugs or therapies.
, , covers how we ensure the safety and efficacy of new drugs and therapies before they are available for patients.
, , dives into the world of the pharmaceutical supply chain workflow and introduces you to some challenges in getting new drugs and therapies to patients around the world in a timely manner.
, , summarizes some key challenges, including the regulatory and technical aspects, that deter organizations from adopting machine learning in healthcare and life sciences applications.
, , is all about the future of AI in healthcare and life sciences. We will review some trends in the world of AI/ML and its applications in the healthcare and life sciences industry, understand what’s influencing these trends, and see what may lie in store for us in the future.
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.
Download the example code files
You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Applied-Machine-Learning-for-Healthcare-and-Life-Sciences-using-AWS. If there’s an update to the code, it will be updated in the GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots and diagrams used in this book. You can download it here: https://packt.link/nGhXe.
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...
