Build real-world artificial intelligence apps on AWS to overcome challenges faced by healthcare providers and payers, as well as pharmaceutical, life sciences research, and commercial organizations
Key Features
- Learn about healthcare industry challenges and how machine learning can solve them
- Explore AWS machine learning services and their applications in healthcare and life sciences
- Discover practical coding instructions to implement machine learning for healthcare and life sciences
Book Description
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.
What you will learn
- Explore the healthcare and life sciences industry
- Find out about the key applications of AI in different industry segments
- Apply AI to medical images, clinical notes, and patient data
- Discover security, privacy, fairness, and explainability best practices
- Explore the AWS ML stack and key AI services for the industry
- Develop practical ML skills using code and AWS services
- Discover all about industry regulatory requirements
Who this book is for
This book is specifically tailored toward technology decision-makers, data scientists, machine learning engineers, and anyone who works in the data engineering role in healthcare and life sciences organizations. Whether you want to apply machine learning to overcome common challenges in the healthcare and life science industry or are looking to understand the broader industry AI trends and landscape, this book is for you. This book is filled with hands-on examples for you to try as you learn about new AWS AI concepts.
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
Ujjwal is a Principal AI/Machine Learning Solutions Architect at AWS where he leads the machine learning solutions architecture group dedicated to healthcare and life sciences. Over the years, Ujjwal has been a thought leader in the healthcare and life sciences industry, helping multiple Global Fortune 500 organizations achieve their innovation goals by adopting machine learning. His work involving the analysis of medical imaging, unstructured clinical text and genomics has helped AWS build products and services that provide highly personalized and precisely targeted diagnostics and therapeutics. Ujjwal's work has also been featured in multiple global conferences, peer-reviewed publications or technical and scientific blogs.
