Book Image

Natural Language Processing with AWS AI Services

By : Mona M, Premkumar Rangarajan
Book Image

Natural Language Processing with AWS AI Services

By: Mona M, Premkumar Rangarajan

Overview of this book

Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production. To start with, you'll understand the importance of NLP in today’s business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic. Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications.
Table of Contents (23 chapters)
1
Section 1:Introduction to AWS AI NLP Services
5
Section 2: Using NLP to Accelerate Business Outcomes
15
Section 3: Improving NLP Models in Production

Chapter 12: AI and NLP in Healthcare

In the previous chapter, we covered how AWS AI services can be used to set up a chatbot with your document workflows using Amazon Lex and Amazon Kendra. In this chapter, we will talk about how Amazon Textract and Amazon Comprehend Medical can help digitize medical claims in healthcare. We will talk about the healthcare industry's claims processing system and why it's important to automate medical claims. Then, we will walk you through how you can use Amazon Textract to digitize these claims in paper form and use postprocessing to validate them. Then, we will show you how you can extract NLP insights from these claims, such as whether the person was diabetic or not, using Amazon Comprehend Medical APIs.

For invalid claims, we will show you how to easily set up notifications to notify the person submitting the claims to resubmit it with the right data, such as ZIP code or claim ID. Lastly, we will show you some architecture patterns...